Evaluating Safety and Health Impacts

TDM Impacts on Traffic Safety, Personal Security and Public Health


TDM Encyclopedia

Victoria Transport Policy Institute


Updated 6 September 2019

This chapter describes how road risk is measured, and how to evaluate the safety and health impacts of different TDM strategies. For more detailed analysis of this issue see Litman and Fitzroy, 2005 (www.vtpi.org/safetrav.pdf).




Basic Terms and Concepts. 2

Road Risk and Road Safety. 3

Crashes and Casualties. 4

Insurance Claims. 6

Crash Costs. 6

Crash Frequency and Severity. 9

Crash Rates and Trends. 13

How Mileage Affects Crash Frequency. 16

Other Public Safety and Health Risks. 21

Personal Security. 21

Physical Activity. 21

Safety and Health Impacts of Specific TDM Strategies. 22

Traffic Speed Reductions. 22

Access Management 22

Travel Time and Route Shifts. 23

Mode Shifting. 23

Vehicle Mileage Reductions. 27

Pay-As-You-Drive Vehicle Insurance. 28

Improved Automobile Availability. 29

Vehicle Fuel Efficiency. 29

Mobility Substitutes. 30

Land Use. 30

Improved Personal Security. 32

Safety Education. 32

Safety and Health Impacts Summary. 32

Evaluating Traffic Safety Impacts Of Vehicle Travel Changes. 33

Highway Improvements. 33

Vehicle Safety Improvements. 34

Fuel Efficiency Standards. 35

Conclusions. 35

Related Chapters. 37

References And Resources For More Information. 37




A paradigm shift (a fundamental change in the way problems are defined and potential solutions evaluated) is occurring in transport safety analysis. The old paradigm assumes that “normal” vehicle travel is a safe activity, since most accidents are associated with special risks such impaired driving, young or old drivers, or hazardous roadway conditions. From this perspective, efforts to reduce overall vehicle travel to increase safety are inefficient and unfair because they “punish” all motorists for dangers created by a minority. As a result, the old paradigm emphasizes targeted safety programs intended to reduce high-risk driving activities, plus improve vehicle occupant protection to reduce injuries when crashes occur. Such programs can be considered successful when risks are measured per unit of travel (such as per billion vehicle-kilometers), but are less successful when measured per capita because their safety benefit has been offset by more total vehicle travel. Overall, the U.S. has a significantly higher per capita crash rate than most peer countries largely due to more per capita vehicle mileage.


The new safety paradigm recognizes that all vehicle travel incurs risks, and that high-risk and low-risk driving are complements: transport and land use policies that increase per capita vehicle travel inevitably increase high-risk driving. For example, in automobile-dependent communities people often drive to events where alcohol is consumed, most young males have drivers’ licenses and cars, and seniors continue driving despite declining ability because mobility alternatives are unavailable and stigmatized. The new paradigm recognizes the safety benefits of both targeted programs and transportation demand management strategies that reduce total vehicle travel.


Although not all experts understand or endorse the new safety paradigm it is gaining accepted. For example, the Federal Highway Administration 2010 Transportation Planner's Safety Desk Reference (FHWA 2012) recognizes that, “By providing mobility alternatives to the auto, transit reduces vehicle miles traveled (VMT), resulting in fewer traffic incidents, injuries, and fatalities. Transit ridership can be encouraged among the groups with the highest crash rates, such as young and older drivers, to reduce the potential for crashes.” That is a major step toward recognizing TDM as a traffic safety strategy. However, the Safety Desk Reference provides no guidance on how to calculate mobility management safety benefits or incorporate TDM into traffic safety programs.


The new paradigm supports more integrated and beneficial planning. Most conventional safety strategies impose significant costs and provide few other benefits. For example, driver impairment reduction strategies require restrictive drinking policies and increased policing, improved vehicle crash protection adds equipment costs and vehicle weight, and reducing roadside hazards often involves more costly roadway engineering and loss of roadside trees. In contrast, most TDM strategies provide significant co-benefits including congestion reduction, road and parking facility cost savings, consumer savings, energy conservation and emission reductions, and improved mobility for non-drivers, and improved public fitness and health, in addition to increased safety.


The table below summarizes these impacts. Most conventional transport safety and health strategies provide limited benefits. Most TDM strategies provide various safety, health and other benefits, and so are justified by more comprehensive analysis.


Table 1            Transport Safety and Health Strategies Impact Summary





Basic Access

Other Impacts

Conventional Safety and Health Strategies


Targeted safety programs

Large benefits

No benefit

No benefit

No benefit

More regulations and program costs

Crash protection

Large benefits

No benefit

No benefit

No benefit

Equipment costs and heavier vehicles

Road safety design

Moderate benefits

No benefit

No benefit

No benefit

Increased roadway costs, loss of trees

Efficient and alt. fuel vehicles

No benefit

Large benefits

No benefit

No benefit

Varies. Energy conservation

Exercise and sport promotion

No benefit

No benefit

Large benefits

No benefit

Increased user enjoyment

Transportation Demand Management Strategies


Traffic calming and speed control

Large benefits

Mixed. Can increase local emissions

 Large benefit

Large benefit

Program costs. Lower travel speeds

Active transport improvements

Benefits if programs increase walking & cycling safety

Large benefits

Large benefits

Large benefits

Program costs. Reduced congestion. User enjoyment

Public transit improvements

Large benefits

Large benefits

Large benefits

Large benefits

Program costs. Reduced traffic and parking congestion.

Transport pricing reforms

Large benefits

Large benefits

Large benefits

Mixed. Can improve travel options.

Additional user costs. Revenues. Reduced traffic and parking congestion

Mobility management marketing

Moderate benefits

Moderate benefits

Moderate benefits

Small benefits

Program costs. Reduced traffic and parking congestion

Smart growth development policies

Large benefits

Mixed. Reduces emissions but may increase proximity

Large benefits

Large benefits

Various costs and benefits

This table summarizes the impacts of various traffic safety strategies, including TDM.



Basic Terms and Concepts

Road Risk and Road Safety

Road risk is a general term for the costs to society of road traffic crashes.


Road safety refers to a reduction in road risk and crash costs. There are various ways to improve road safety:

·         Reduce total vehicle mileage.

·         Reduce per mile crash rates (more caution drivers, safer roads).

·         Reduce Traffic Speeds.

·         Improved vehicle occupant protection (energy absorbing vehicle designs, seat belt use, helmet use, air bags).

·         Improved emergency response and trauma care.

·         Improved long-term medical treatment and rehabilitation for traffic victims.

·         Reduced vehicle repair costs.


Reduced Crashes Versus Safe Crashes

There are two general approaches to increasing road safety. One is to prevent crashes, for example, by reducing vehicle traffic volumes and speeds, designing and building roadways with fewer conflicts (such as grade-separating intersections and replacing traffic signals with roundabouts), increasing traffic law enforcement, reducing intoxicated and other high-risk types of driving, and imposing more rigorous requirements on driving privileges. These strategies are intended to promote crash-free traffic operations.


Another approach to road safety involves reducing the amount of damage that occurs in a crash, particularly for vehicle occupants, by building crash protection into roadways (wider shoulders, break-away light standards, etc.) and in vehicles (increased vehicle weight, energy absorbing vehicle design, air bags, etc.), and by requiring vehicle occupants to use seatbelts, child restraints, motorcycle and bicycle helmets. These strategies are intended to mitigate crash injuries. Critics call this approach “safe crashing” (Birnbaum). Some argue that this approach has been overemphasized by government policies and traffic safety agencies (Gladwell 2001). For example, at the Federal Highway Administration’s Transportation Planner's Safety Desk Reference (FHWA 2010) includes no consideration of demand management (reductions in vehicle travel, particularly by higher risk drivers) as a safety strategy.


As described later in this chapter, “safe crashing” safety strategies tend to have a Rebound Effect: as individual drivers feel safer they take somewhat greater risks, which offsets some of their expected safety benefits, and may increase risk to others, particularly vulnerable road users such as pedestrians, cyclists and motorcyclists. As a result, net safety benefits may be smaller than expected. This is called risk compensation or risk homeostasis.



Crashes and Casualties

Traffic safety researchers measure crashes (also called collisions, accidents or incidents), injuries, fatalities and damages. Injuries and fatalities together are called casualties. Many road safety experts prefer the term crash to accident, because “accident” implies a random event, while “crash” emphasizes that such events have a cause (driver error, mechanical failure, poor roadway design, etc.) and so are preventable.


Traffic crash Statistics are available from the following sources:


·         Bureau of Transportation Statistics (www.bts.gov). The BTS National Transportation Statistics report provides annual information on transportation activities and impacts, including traffic crashes. North American Transportation in Figures, provides crash data for Canada, Mexico and the U.S.


·         National Highway Traffic Safety Administration (www.nhtsa.dot.gov) provides comprehensive information on traffic crashes and safety programs in the U.S.


·         Global Road Safety Partnership (www.grsproadsafety.org) and the Disease Control Priority Project (www.dcp2.org), are international partnerships working to improve safety in developing and transition countries, which provide traffic crash and safety data.


·         National Center for Statistics and Analysis (www.nhtsa.dot.gov/people/ncsa) collects and analyzes traffic crash data.


·         Transport Canada (www.tc.gc.ca/roadsafety) provides Canadian traffic crash data.


·         European Conference of Ministers of Transport (www.oecd.org/cem/stat) provides traffic crash data for European countries.


·         G-7 Transportation Highlights (www.bts.gov/itt/G7HighlightsNov99/G-7book.pdf) provides transportation data for European countries, the U.S., Canada and Japan.


·         International Road Traffic and Accident Database, (www.bast.de/htdocs/fachthemen/irtad//english/we2.html) provides international crash data.



Data used to evaluate transport risks often different between jurisdictions, times and modes. For example, some data sets only include deaths that occur at a crash site, others include deaths within a certain number of days or months. Some transit and rail fatality data include suicides (which represent a significant portion of rail fatalities), and passengers who suffer a heart attack or assault on a transit vehicle or train stations.



Crash Severity Scales (FHWA, 1994)

Crashes are categorized by severity using indices such as the KABC Scale and the Abbreviated Injury Scale, as illustrated in tables 2a and 2b.


Table 2a

KABC Scale



Cost (1994)








Injury Evident



Injury Possible



Property Damage Only



Table 2b

Abbreviated Injury Scale (AIS)



Cost (1994)



















These tables show two commonly-used crash severity indices.



Police and traffic safety organizations collect traffic crash and casualty data (National Center for Statistics and Analysis), but many crashes are never reported, particularly minor Property Damage Only (PDO) crashes, and injuries to pedestrians and cyclists (James 1991). Crash statistics may reflect either reported crashes, or estimates of total crashes calculated by multiplying reported crashes by an estimate of the portion of crashes that are unreported.


Insurance Claims

Insurance actuaries measure claims and claim costs (insurers’ expenses for managing and compensating crash damages). Less than half of all vehicle insurance claims are crash-related (glass damage, fire, theft and vandal damage do not usually involve a crash). About 70% of crashes involve multiple vehicles, which usually result in multiple claims. An average crash produces about 1.5 insurance claims, so a 10% reduction in total crashes reduces crash-related claim costs by 15%, all else being equal.


Crash Costs

Crash costs refer to damages (also called losses) caused by collisions, and costs of crash damage avoidance activities. Total crash costs include both of monetary and non-monetary losses. Monetary costs include damages to vehicles, medical costs, lost productivity due to disabilities and death, emergency services, and expenditures on safety programs and equipment to reduce crash damages. Non-monetary costs include pain, grief and lost quality of life due to crash injuries and deaths, and reduced mobility to non-motorized modes due to crash risk. Several techniques are be used to estimate the value that people place on these non-monetary costs, resulting in various estimates of the total cost per injury or death, as indicated in tables 1a and 1b. Table 3 summarizes an estimate of total U.S. crash costs.


Table 3            U.S. Crash Costs (1997) (Wang, Knipling and Blincoe 1999)


All Vehicles

Passenger Cars


Light Trucks/Vans

Single Unit Trucks

Combination Trucks

Police Reported Crashes







Minor-Moderate Injuries







Serious-Fatal Injuries







Per 100 Million VMT







Per 1,000 Vehicles







Cost Per Crash







Cost Per Vehicle-mile







Cost Per Vehicle-Year







This table summarizes results from one study of U.S. crashes and crash costs.



The value to society of avoiding crashes tends to be far greater than compensation costs, because it would be poor public policy to fully compensate all crash damages, since that reduces motorists’ incentive to drive defensively. Overly generous crash compensation may encourage people who place a low value on their own disability to intentionally injure themselves in a vehicle crash.


However it is measured, road risk represents a major cost to society. A National Highway Traffic Safety Administration study estimated Human Capital (which only reflects market costs such as property damage, medical treatment, and lost productivity) 2010 U.S. crash costs totaled $277 billion or $897 per capita, and $871 billion in total costs (including non-market costs) (Blincoe, et al. 2014). Of these costs, approximately three-quarters are considered external to individual drivers involved in a crash. The report also incorporates Quality-Adjusted Life Years (QALYs), which reflect non-market costs such as pain, grief and reduced quality of life.  This averages about $1,500 annually per vehicle, more than most other costs associated with motor vehicle use, as illustrated in Figure 1. Only vehicle ownership expenditures (depreciation, insurance and registration) represent a larger cost category.


Figure 1          Costs of Motor Vehicle Use in the U.S. (Transportation Costs)

This figure illustrates the estimated magnitude of various costs of motor vehicle use. Crash damages are one of the largest costs, far greater than traffic congestion or environmental costs.



Comparing worldwide health risks, a major study by Murray, et al (1996) finds that traffic crashes are the eighth leading cause of death and disability in developed countries, and the tenth in developing countries. Among adults aged 15-44, traffic crashes are the leading cause of injury death for men and the fifth leading cause for women. The study projects that traffic crashes could increase to become the third most common cause of death worldwide if present trends continue. Kopits and Cropper (2003) use international historical trend data to estimates that currently about 720,000 people die annually in traffic crashes and this will likely increase to about 1.2 million in 2020.


That crashes are one of the largest transportation costs would probably not surprise most people. Although traffic crashes are uncommon events, their effects can be devastating. Traffic crashes are the most common cause of death and disability for people in the prime of life. Many consumers demonstrate the high value they place on traffic safety by paying hundreds of dollars annually to drive a safer vehicle or purchase optional insurance coverage.


Crash costs are widely distributed. A portion is borne directly by the occupants of a vehicle involved in a crash, and so is considered an internal cost. This includes uninsured damages and insurance deductibles borne by motorists involved in a crash. Crash costs compensated by vehicle insurance are external to individual motorists but internal to motorists as a group. Other crash costs are broadly distributed through society, and so are considered external. These include uncompensated medical, disability, lost productivity and grief costs to people who are not vehicle occupants, crash avoidance costs to pedestrians and cyclists, and public costs for traffic safety programs and emergency services. Table 4 categorizes the distribution of various types of crash costs.


Table 4            Crash Cost Distribution






Safety equipment costs, uncompensated damages, insurance deductibles borne by the motorists involved in a crash.

Uncompensated pain and lost quality of life borne by the motorists involved in a crash.



Damages and lost income compensated by insurance.

Pain, grief and lost quality of life compensated by insurance.



Uncompensated damages borne by nonmotorists; emergency response and crash prevention programs, reduced walking and cycling.


Uncompensated pain, grief and lost quality of life borne by nonmotorists.

This table shows how crash costs are distributed.



The distinction between internal and external costs is important for evaluating the overall effectiveness, efficiency and equity of crash compensation and traffic safety measures (Market Principles). Some actions shift crash costs from one group to another. For example, driving a heavier vehicle tends to reduce risk to that vehicle’s occupants (internal risk) but increases risk to other road users (external risk). Traffic safety measures that make drivers feel safer tend to encourage more “intensive” driving that increases risk to other road users, particularly pedestrians and cyclists (Rebound Effects). Although both drivers and nonmotorized road users (pedestrians and cyclists) may make errors that contribute to crashes, nonmotorized road users bear a much greater portion of crash costs because they are more vulnerable to injury.


As a result of these crash cost distribution factors, safety strategies considered optimal from an individual’s perspective may be quite different than what is optimal from society’s perspective, since individuals value solutions that shift risk to others but society does not. For example, individuals may compete for safety by shifting from walking to driving, or purchasing ever-larger and heavier vehicles, but this is economically inefficient if it does not result in an overall reduction in crash costs, and it is inequitable if it simply shifts risks to more vulnerable or lower-income people. For example, many consumers purchase large vehicles because the feel safer to drive, but they increase risk to occupants of other vehicles, resulting in little net benefit to society (Wenzel and Ross, 2002).


Traffic safety analysis is complicated by the tendency of risks to maintain equilibrium, that is, when accident damages are considered excessive, individuals and society react with additional safety strategies until the risk reaches a more acceptable level, called Rebound Effects, offsetting behavior, or target risk (Adams 2010; Vanderbilt 2008; Wilde 1994). This occurs in various ways, for example, through the implementation of safety programs targeting geographic areas, demographic groups, or travel modes that are considered high risk, therefore bringing them down to an acceptable risk level, and because individual motorists may become more cautious under more hazardous driving conditions, or after somebody they know is killed in a crash. Conversely, motorists tend to take small additional risks when they feel relatively safe, such as driving faster and talking on the telephone while driving under “normal” conditions, and deferring the replacement of warn tires until winter when driving conditions become more hazardous. Similarly, motorists who feel safer due to increased crash protection tend to drive more intensely, meaning that they choose higher speeds, leave less distance between their vehicles and other objects along the roadway, and in other ways take additional risks. As a result, it can be difficult to ascertain the safety impacts of a particular strategy or program.


Crash Frequency and Severity

Crashes are relatively infrequent events. A vehicle is likely to be involved in a crash-related claim approximately once every 14 years or quarter-million kilometres of travel, and a driver is likely to be found culpable (at fault) in only about half of the crashes they are involved in, or about once every half-million kilometres driven. Most motorists drive for decades without causing a crash, and even relatively high-risk motorists drive most years without having an at-fault insurance claim. As a result, a large number of vehicle-years worth of data are needed for statistically-reliable analysis of crash rates.


Crash frequency and severity is affected by various factors described below:


Roadway Characteristics

Crash rates increase with traffic density (vehicles per lane-mile), but crash severity tends to increase with vehicle speeds. Zhou and Sisiopiku (1997) find that crash rates are lowest on moderately congested roads (a volume to capacity ratio of 0.6), and increase at lower and higher congestion levels. As a result, crashes per vehicle-mile tend to be greater in urban areas, but fatalities per vehicle-mile tend to be greater on uncongested, rural roads (Janke 1991; Stuster and Coffman 1998). Table 5 shows how crash rates vary by road type in one jurisdiction. Crash rates are three times higher for urban driving but fatality rates are more than twice as high for rural driving.


Table 5            Crashes Per 100 Million Vehicle-Miles (FHWA, “Fatality Rates by Road Function,” 2000)







Principle Arterial



Minor Arterial



Major Collector



Local Road






Although crash rates tend to be higher in urban areas, rural crashes tend to be more severe, resulting in higher fatality rates.



Driver Characteristics

Kweon and Kockelman (2003) calculate per-mile crash rates for various categories of drivers and vehicles, and find that young and middle-aged men are slightly more likely to have a crash than their female counterparts, but this is reversed in older age groups.  Professional drivers (truck, bus, taxi, etc.) tend to have low per-mile crash rates, but relatively high crash rates per vehicle-year because of their high annual mileage. Younger and older drivers tend to have relatively high crash rates per vehicle-mile, but this tends to be offset by their relatively low average annual miles of travel. Intoxicated drivers tend to have crash rates many times higher than sober drivers per vehicle-mile. In 2000, 31% of all traffic fatalities involved at least on intoxicated driver (those with blood alcohol concentration exceeding 0.10 g/dl).


Vehicle/Mode Characteristics

Kweon and Kockelman (2003) find that cars have higher crash frequencies than light-duty trucks (including SUVs, pickups and mini-vans), but light-duty trucks have more rollover crashes, which tend to have high disability and fatality rates. Vehicle design features affect crash frequency and severity.


·         Newer vehicles tend to have design features and safety equipment that provide greater crash protection.


·         Heavier vehicles tend to provide greater protection to their occupants but cause greater damage when they hit smaller vehicles.


·         Buses and other transit vehicles tend to have low crash rates per mile, and have low injury rates for occupants.


·         Sport Utility Vehicles and large vans tend to have a high rate of roll-over crashes.


·         Motorcycles, bicycles and pedestrians (called vulnerable road users) tend to have greater injuries when involved in a crash.



Table 6            U.S. Transportation Fatalities, 2001



Veh. Travel


Pass. Travel

Fatality Rate





Bil. Miles


Bil. Miles



Passenger Car


















Trucks – Light









Trucks – Heavy









School & Intercity Bus









Intercity Bus









Commercial Air









Transit Bus









Heavy Rail









Commuter Rail









Light Rail



























Occ. = Occupants. Based on BTS, Tables 1-32, 2-1 and 2-4; APTA, Safety Summary By Mode.



Table 6 compares crash risks between different travel modes. Motorcycles have extremely high fatality risk. Automobile occupants have modest risk. Transit passengers have relatively low risk, although total risk of transit travel (including fatalities to non-occupants and employees) is somewhat higher, as indicated in Table 7. Data on pedestrian and cyclist mileage is unavailable so it is not possible to calculate their fatality rate per billion miles of travel.


Table 7           U.S. Transit Fatalities, 1999 (APTA)



Commuter Rail

Demand Response

Heavy Rail

Light Rail

Trolley Bus



Fatalities (Excludes Suicides)


































Fatality Rate Per Billion Passenger Miles


































This table shows crash fatalities and fatality rates for various types of transit. Fatality rates can vary because different types of transit function in different conditions.



Empirical evidence indicates that shifts from driving to nonmotorized modes can reduce total per capita crash risk. Jacobsen (2003) found that per capita collisions between nonmotorized travelers (pedestrians or cyclists) and motor vehicles decline in areas with higher rates of nonmotorized travel, suggesting that drivers become more cautious when they see more walkers and cyclists. The author calculates that the number of motorists colliding with pedestrians and cyclists increases at roughly 0.4 power of the number of people walking or cycling (e.g., doubling NMT travel in a community will increase pedestrian/cycling injuries by 32%), and the probability that a motorist will strike a nonmotorized traveler declines with the roughly -0.6 power of the number of people walking and cycling in a community (e.g., as a pedestrian, my risk of being hit by a motor vehicle declines 34% if walking and cycling double in my community). Robinson (2005) found similar results using Australian data: doubling bicycle travel reduces cyclist risk per kilometer by about 34%; and conversely, halving bicycle travel increases risk per kilometer about 52%.


Land Use Characteristics

Some research indicates that per-capita traffic risk increases with sprawl. Ewing, Schieber and Zegeer (2003) found that for every 1% increase in their sprawl index (i.e., more compact, less sprawl), all-mode traffic fatality rates fell by 1.49% (P < .001) and pedestrian fatality rates fell by 1.47% to 3.56%, after adjustment for pedestrian exposure. This is explained by the higher per capita annual mileage, higher traffic speeds and less consideration of pedestrian safety in sprawled areas.


Is A Risk “Significant”? (Ezra Hauer, 2003)

Traffic safety experts use various analysis methods to determine whether a particular change in crash rates is statistically “significant,” meaning that in a particular situation differences are measurable. In traffic safety research there is often insufficient data to satisfy a Null-Hypothesis-Significance-Test, and so the results are said to not be significant. This statement is often misinterpreted to mean that the no impact has occurred or that the results are not important, or that the results prove that there is no effect, rather than that the study is simply inadequate to give statistical confidence to results. Below are a few examples.


  • A 1976 study found that after right-turn-on-red was allowed at twenty intersections in Virginia, the number of crashes increased 9% from 308 to 337, and the number of pedestrian injuries increased 4% from 69 to 72. These were determined not to be statistically significant, due to the relatively small numbers. As a result, policy-makers were told that “no significant increase in crashes has been noted.” This was intended to explain that the data were insufficient, but it was interpreted to mean that right-turn-on-red has no effect.


  • A 2001 study found that the addition of paved shoulders on a highway reduced most types of crashes, but due to limited data the researchers concluded that, “The study could not discern any statistically significant differences in either crash rate or severity rate between two- and four-foot shoulder installations…this study does not show the increased construction cost of four-foot shoulders on state routes to be justified by an increase in traffic safety.” These results can be misinterpreted to mean that the study proved that wider shoulders do not increase safety.


  • A 2001 study found that in most cases, increased highway speeds had no significant impact on crash fatalities when each of the fifty states were analyzed individually. This was interpreted to mean that higher traffic speeds do not increase risk. But when evaluated together, the data do indicate that raising speed limits increases crash fatalities.



Crash Rates and Trends

How an activity is measured can have a significant impact on how it is evaluated (Measuring Transportation). This is true of transportation safety and health impacts. Figure 2 illustrates U.S. traffic fatality rates between 1960 and 2000. This data can be interpreted in two different ways, giving two very different conclusions concerning the magnitude of traffic risk and how traffic safety can be improved.


Figure 2          U.S. Traffic Fatalities (BTS, 2000)

This figure illustrates traffic fatality trends over four decades.



Transportation professionals often evaluate road risk based on crash and fatality rates per unit of vehicle travel (e.g., per hundred million vehicle-miles or kilometers). Measured in this way, vehicle travel is a relatively safe activity, most vehicle trips have a tiny crash risk, and crash rates declined by more than two thirds over the last four decades indicating that current traffic safety programs are successful. This suggests that the best way to reduce road risk further is to continue applying the same strategies that worked so well in the past.


But per capita vehicle mileage has more than doubled over the last forty years, which has largely offset the decline in per-mile crash rates. When fatalities are measured per capita (e.g., per 10,000 population), as with other health risks, there has been surprisingly little improvement over this period despite tremendous investments in safer roads and vehicles, large increases in the use of seatbelts and other safety devices, significant reductions in drunk driving, and improvements in emergency response and trauma care. Taking these factors into account, much greater casualty reductions should have been achieved. Traffic crashes continue to be the greatest single cause of deaths and disabilities for people in the prime of life. Although the U.S. has one of the lowest traffic fatality rates per vehicle-mile, it has one of the highest traffic fatality rates per capita, as illustrated in Figure 3. From this perspective, traffic safety continues to be a major problem, current safety efforts are ineffective, and new approaches are needed to really improve road safety.


Figure 3          International Traffic Fatality Rates (OECD, IRTA Database (www.bast.de/htdocs/fachthemen/irtad//english/we2.html), 2001)

This figure compares national traffic fatality rates. The U.S. has one of the lowest rates per vehicle-kilometer and one of the highest rates per capita.



Annual traffic fatality rates in various jurisdictions typically range from about 2 to 20 annual deaths per 100,000 population, a 0.15% to 1.5% lifetime risk for an average individual. Each fatality is estimated to represent 15 severe injuries requiring hospital treatment, 70 minor injuries, and about 150 property damage only traffic crashes, so the lifetime chance of a crash injury typically ranges from 2% to 22% (WHO, 2004).


When road risk is measured using a mileage-based rate, increased mileage is not considered a risk factor and traffic reductions are not considered a safety strategy. From this perspective, an increase in total crashes is not a problem provided that there is a comparable increase in mileage. Increased vehicle travel can even be considered a traffic safety strategy if it occurs under relatively safe conditions, because more safe miles reduce per-mile crash and casualty rates. For example, building grade-separated highways tends to reduce per-mile crash rates because they have low per-mile crash rates and encourage increased driving. However, they may do little to reduce per capita crashes if they increase total vehicle travel without reducing travel on less-safe roads.


By emphasizing per-mile crash rates, conventional transportation safety analysis tends to ignore Transportation Demand Management (TDM) as a traffic safety strategy, although from a public health perspective, TDM can offer significant safety benefits.



HOW WE DRIVE; Roads Are Safer; Cars Are Safer. Drivers? Forget It.

By John M. Broder


Dr. Evans, who is the president of the International Traffic Medicine Association, contends that so-called safety devices in cars, particularly air bags, have had an insidious and deadly effect on driver behavior. He said that as recently as the late 1970s the United States had the safest highways, using the measure of traffic deaths per 100,000 registered vehicles. Today, he said, the United States is in 12th place and sinking. “If the United States had simply matched Canada’s performance over that period,” Dr. Evans said, “annual U.S. fatalities this year would be 28,000, rather than more than 41,000.”


He said that since the mid-60’s, American have spent billions of dollar seeking the perfect technological fix to prevent fatalities. Their solutions, the air bag and other “passive” devices, have only compounded the problem. Other industrial nations, Dr. Evans said, have pursued a more balance approach -- better and early driver education, stricter enforcement of traffic and seat-belt laws, use of cameras to detect speeding and red-light running and campaigns against aggressive driving.


“We have just receive the wonderful good news that the air bag is killing fewer people than it used to,” he said. “When was that an advertisement for a safety device, that it’s killing fewer people than it used to?”


Dr. Evans said that the air bag and other safety devices had the same effect collectively as advances in cardiac medicine. Angioplasty and bypass surgery have not decreased the rate of death from heat disease, he said and might have convinced people that there is a technological “cure” for the unhealthy behaviors that lead to heart attacks.


“We see American collectively driving a couple of miles an hour faster because of a false sense of security,” he said. “And that collective increase in speed more than washes away the alleged benefit of air bags.”



How Mileage Affects Crash Frequency

Annual crash risk can be considered the product of two factors: per-mile crash risk times annual mileage. Changing either factor affects annual crash risk. Although per-mile crash rates vary depending on various factors related to driver, vehicle and geographic conditions, most of these risk factors do not change with annual mileage. A high-risk motorist may crash every 50,000 miles, while a lower-risk driver may crash every 500,000 miles, but in either case reducing annual mileage reduces annual crashes. Even a driver who never violates a traffic rule faces risks beyond their control – errors by another driver, an animal running into the roadway, catastrophic mechanical failure, a sudden medical problem – and most drivers take minor risks that have small but real chances of causing a crash. Not only does increased mileage increase an individual motorist’s chances of having a crash, it also increases traffic density (vehicles per lane-mile) which increases per-mile crash rates, as described earlier.


There is considerable empirical evidence that crashes increase with annual vehicle mileage and that reductions in annual vehicle mileage reduce crashes and crash costs. Balkin and Ord (2001) found seasonal cycles in U.S. highway fatalities that correlate with monthly variation in vehicle mileage. Lovegrove and Sayed (2006) found that neighborhood crash rates decline with reductions in total vehicle traffic. 


Until recently, information on the relationship between annual vehicle mileage and annual crashes was only available in highly aggregated form. Recently, some new data sources have allowed more detailed analysis. Figure 4 illustrates the relationship between annual mileage and crash rates for vehicles in the Vancouver, BC region, based on mileage readings collected during emission inspections that were matched with individual vehicles’ insurance claims for more than 500,000 vehicle-years (Litman, 2009).


These data show a strong positive relationship between annual mileage and annual crashes. The same patterns were found when the data were disaggregated by demographic and geographic factors (Litman, 2009). The results indicate that, all else being equal, crash and insurance claim rates increase with annual mileage.


Figure 4          Crash Rates by Annual Vehicle Mileage (Litman, 2009)

Crashes per vehicle tend to increase with annual mileage.


A number of factors tend to partly offset this relationship for groups of vehicles (Janke, 1991):


·         Motorists who are higher-risk per vehicle-mile due to age or disability tend to drive lower annual mileage, while high annual-mileage motorists tend to be relatively capable drivers.


·         Newer, mechanically safer vehicles tend to be driven more each year than older vehicles.


·         Urban drivers tend to have higher crash rates and lower annual mileage.


·         High mileage motorists tend to do a greater share of driving on safer, grade-separated highways.


·         There may be other types of offsetting behaviors, by which higher-mileage drivers take more precautions to limit their risk.



Because of these offsetting factors, the relationship between mileage and crashes is much stronger for individual drivers than for groups of drivers. Such factors result in the crash/mileage curves that level off for high annual mileage groups, as indicated in Figure 4. The curves imply that, once a motorist drives about 25 thousand kilometers a year, there is little additional risk to additional driving. However, these factors represent differences between different motorists. Most of the offsetting factors listed above do not change when an individual driver reduces annual mileage. For example, a motorist who reduces from 12,500 to 11,500 annual miles in response to a TDM strategy such as Commuter Financial Incentives, Parking Pricing or Distance-Based Vehicle Charges is unlikely to become less skilled, take greater chances or drive a less safe vehicle.


Because a particular driver tends to maintain a relatively constant level of risk, a change in annual mileage tends to cause a similar change in that individual’s annual crash rate. Whether a motorist is relatively high- or low-risk, a reduction in their annual mileage is likely to cause a proportional reduction in that vehicle’s chance of having a crash and insurance claim. As a result, a given change in annual mileage tends to cause a proportional change in that vehicle’s chance of being involved in a crash. Since most crashes involve multiple vehicles, a change in mileage can produce a proportionally greater change in total crash costs (Vickrey 1968; Edlin 1999). As a result, a 1.0% reduction in vehicle-mileage can cause a reduction in crash costs that exceed 1.0%.


For example, if you reduce your chances of causing a crash by 10% (perhaps by having a vehicle with better steering and braking ability, or by driving more cautiously), your total crash risk declines by about 5%, since other drivers cause about half of the crashes you are involved in. If your annual mileage declines by 10%, your chance of causing a crash declines by 10%, and your risk of being in a collision caused by other drivers’ mistakes also declines, since you are no longer a crash target for those miles. If all other motorists reduce their mileage by 10%, but you do not, you can expect a 7% reduction in crash risk, since 70% of your crashes involve another vehicle. If all motorists reduce mileage by 10% and other factors are held constant, total crash costs should decline by about 17% (10% + 7%).


Put another way, reducing mileage provides an external safety benefit to other road users: you are safer if your neighbors reduce their annual mileage because you are less likely to crash, regardless of which driver’s errors contribute to the collision. Reduced mileage reduces traffic density, which reduces per-mile crash risk for all motorists in an area. Note that this analysis is not affected by which vehicle is legally responsible for causing a crash; even a perfect driver who never makes an error reduces total crash risk when they reduce their mileage because they reduce the chance that they will be hit by another motorist or involved in a crash beyond their control that would not occur if they were not on the roadway.


Three factors may partly offset the additional crash risk from increased mileage. First, increased mileage can increase traffic congestion, which, although it increases crash frequency, can reduce crash severity and therefore traffic fatalities. Second, drivers may be more cautious when traffic density increases. Third, increased mileage may justify increased roadway investments, leading to more driving on roadways with lower per-mile crash rates, such as grade-separated highways. As a result, the elasticity of vehicle travel to crash costs may be somewhat lower than 1.7 (i.e., a 10% increase in mileage causes less than a 17% increase in crash costs).


Empirical research supports the contention that changes in total annual vehicle mileage cause proportionately larger changes in crash costs. Analyzing state-level insurance claim data in the U.S., Edlin (1999) found that the elasticity of vehicle insurance costs with respect to mileage is between 1.42 and 1.85, meaning that a 10% reduction in vehicle mileage reduces crash costs between 14% and 18%. Other studies reviewed in Edlin’s paper support this estimate.


It is possible that a travel reduction strategy could produce proportionally smaller crash reductions if it reduced low risk driving more than higher-risk driving. For example, reductions in just freeway driving or just sober driving could cause a less than proportional reduction in crashes. The empirical evidence described above indicates that broad reductions in driving do reduce overall crash rates, and because most serious crashes involve more than one vehicle, a given mileage reduction tends to produce a proportionally larger reduction in total crash costs and casualties.


One-third to one-half of crash reduction benefits caused by mileage reductions accrue to other motorists and their insurers. As a result, significantly greater vehicle travel demand management efforts are justified on safety grounds than are perceived by individual motorists or individual insurance companies. Vickrey (1968) and Edlin (1998) discuss this concept, pointing out that marginal cost pricing of crash costs would generally result in revenues that exceed insurance claim costs.


How Many Vehicle Miles Must be Reduced, On Average, To Eliminate a Traffic Fatality?
Analyzing the Relationship Between Mileage and Crashes
By Allen Greenberg


There were 42,387 highway fatalities and 2.7498 trillion vehicle-miles traveled in the U.S. in 2000, averaging one fatality every 64,873,554 vehicle-miles. Multiple vehicle crashes accounted for 45% of all motor vehicle fatalities, while single vehicle crashes (42% of fatalities) and vehicles crashing into pedestrians (13% of fatalities) accounted for the rest. For single vehicle crash fatalities, a 10% reduction in VMT could be expected to result in a 10% fatality reduction from single vehicle and vehicle/pedestrian crashes, all other things being equal. For multiple vehicle crash fatalities, a 10% reduction in VMT would be expected to result in a 19% fatality reduction among such crashes, since if either of the two vehicles had not been on the road, the fatal crash would most likely not have occurred (there is a 90% chance for each vehicle involved that it would still have been on the road had such a VMT reduction been realized, or a 0.9 X 0.9, or 81% chance, that they both still would have been on the road). Therefore, for every 1,000 fatal crashes resulting from a particular level of VMT, a 10% VMT reduction would initially appear to reduce single vehicle crash fatalities from 550 to 495 and multiple vehicle crash fatalities from 450 to 364.5, saving 140.5 lives through an overall 14.05% reduction in fatalities. 


Further analysis is required if VMT reductions involve Ridesharing, which increases average vehicle occupancy, causing more fatalities and injuries per crash. VMT reductions from Telework, trip consolidation and chaining, and reductions in total personal mileage would not have this effect. To incorporate pricing impacts on vehicle occupancy, mode shift results from Parking Cash Out are used (based on a study involving about 1,700 employees in Southern California): 70% of the decrease in vehicle trips came from increased carpooling, suggesting that a 10% reduction in VMT typically increases vehicle occupancy by 4.86%. While there is one fatality today for every 64,873,554 miles traveled, it now appears that one fatality can be avoided for every 48,417,373 miles of travel reduced (64,873,554/1.405*1.0486), affectively reducing fatalities by 13.4% for every 10% reduction in VMT.


It is possible, of course, that some drivers in fatal multiple vehicle collisions are so reckless and/or impaired that they would simply have crashed into another vehicle and still caused a fatality had they still been on the road but the other vehicle had not been because of MPI. One example of such a fatal crash would be where a motorist runs a red traffic light at high speed and hits and kills the driver of the first car facing a green light proceeding into the intersection. In this case, if the vehicle that previously would have been hit is no longer on the road, the next driver waiting for the green light might have taken his place first in line and still met the same fate. If such a fatality scenario were common, it would be incorrect to discount crash and fatality rates by the full 1.34 times the miles avoided as is done here.  It is assumed, however, that this hypothetical case is a fairly rare one. Further, it would be more than offset by reductions in fatal crashes indirectly related to a first crash (whether fatal or not), where the second crash does not involve any vehicles that are part of the first crash (and thus is reported separately), and where the second crash would not occur if not for the initial crash. A secondary crash of this nature might be caused by insufficient braking time to avoid unexpected congestion from the first crash.



Other Public Safety and Health Risks

Two additional safety and health issues affected by transportation policies are described below.



Personal Security

Personal Security refers to the risk of assault. Transportation decisions affect and are affected by personal security concerns. For example, walking, cycling and transit use tend to decline in areas that are considered insecure, and a factors such as street design and maintenance, transit system management and land use policies in an area can affect the quality of personal security. Some TDM programs include special efforts to Address Security Concerns and reduce these risks. Strategies that increase the number of people walking and cycling on streets and paths, and opportunities for community cohesion, tend to increase security in an area.



Physical Activity

Physical activity refers to physical exercise that improves Public Health. Inadequate physical activity is a major contributor to cardiovascular disease, diabetes, hypertension, obesity, osteoporosis and some cancers. A sedentary lifestyle ranks second only to smoking as a lifestyle risk for disease and premature death, contributing to more than 10% of all deaths in the United States, causing billions of dollars in annual medical costs. Even modest increases in physical activity tend to improve health and reduce mortality rates. The US Center for Disease Control recommends at least 30 minutes of exercise a day, at least 5 days a week, in intervals of ten-minutes or more to stay healthy and maintain existing weight.


Diseases Associated With Inadequate Physical Activity

·         Heart disease

·         Hypertension

·         Stroke

·         Diabetes

·         Obesity

·         Osteoporosis

·         Depression

·         Some types of cancer



Walking and cycling are important types of physical activity. A major study concluded, “Regular walking and cycling are the only realistic way that the population as a whole can get the daily half hour of moderate exercise which is the minimum level needed to keep reasonably fit” (Physical Activity Task Force, 1995). Walking and cycling has become less common in many North American communities. Between 1975 and 1995 the number of walking trips made by an average U.S. resident decline 42%. However, areas with suitable conditions do not experience such declines (Land Use Impacts on Transportation), indicating that transportation and land use policies can affect the amount of physical activity in a community.



Safety and Health Impacts of Specific TDM Strategies

This section describes the safety and health impacts of different types of travel changes caused by TDM.


Traffic Calming and Speed Reductions

Traffic Calming are roadway design features that reduce traffic speeds. Strategies that Reduce Traffic Speeds tend to reduce crash frequency and severity. Even modest speed reductions can prevent many collisions, and reduce the severity of damages and injuries that result when crashes occur (Leaf and Preusser 1998; Stuster and Zail Coffman 1998; Elvik 2001; Gårder 2004; Tranter 2010; Wei and Lovegrove 2010). Speed reductions are particularly effective at reducing injuries to pedestrians and cyclists. The probability of a pedestrian being killed in a crash is 3.5% if the vehicle is traveling at 15 mph, 37% at 31 mph and 83% at 44 mph (Limpert 1994, p. 663). Ernst and Shoup (2009) identify specific ways to improve nonmotorized travel safety.


Tranter (2010) argues that the emphasis in urban areas on increasing vehicle traffic speed and volume contributes to ill-health through its impacts on local air pollution, greenhouse gas production, inactivity, obesity and social isolation. In addition to these impacts, a heavy reliance on cars as a supposedly ‘fast’ mode of transport consumes more time and money than a reliance on supposedly slower modes of transport (walking, cycling and public transport). Using the concept of ‘effective speed’, this paper demonstrates that any attempt to ‘save time’ through increasing the speed of motorists is ultimately futile. If planners wish to provide urban residents with more time for healthy behaviours (such as exercise and preparing healthy food), then, support for the ‘slower’ active modes of transport should be encouraged.


TDM Strategies

Traffic Calming, Speed Reductions, Street Reclaiming, Vehicle Use Restrictions, Streetscaping.



Access Management

Access Management is a set of roadway design principles intended to increase traffic efficiency by reducing the number of driveways and intersections on major roads, and support more efficient land use patterns. It tends to reduce per-mile crash rates, and can improve walking and cycling conditions, encourage transit and ridesharing, and support more efficient land use development. The road safety benefits depend on specific conditions: if Access Management increases vehicle traffic volumes and speeds it may increase total crashes and crash severity. If it reduces roadway conflicts and traffic speeds, supports mode shifting and leads to more Accessible land use, it may increase traffic safety. It tends to be particularly important in developing countries, where informal commercial and residential developing often occurs along roadsides (Vollpracht 2010).

TDM Strategies:

Access Management, Parking Management, Vehicle Restrictions



Travel Time and Route Shifts

TDM strategies that shift vehicle travel from peak to off-peak periods, or from congested highways to alternative routes, have mixed safety impacts. Crash rates per mile are lowest on moderately congested roads, and increase at lower and higher congestion levels, but fatalities decline at high levels of congestion, indicating a trade-off between congestion reduction benefits and crash fatalities (Zhou and Sisiopiku 1997; Shefer and Rietvald 1997). Shifting vehicle trips to less congested roadway conditions can reduce crashes, but the crashes that occur tend to be more severe due to higher travel speeds. As a result, the road safety impacts of TDM strategies that shift travel times and routes can vary, depending on specific circumstances, and are difficult to predict.

TDM Strategies

Flextime, Congestion Pricing, Parking Pricing.



Mode Shifting

TDM strategies that shift travel mode can have a variety of safety and health impacts. Table 8 summarizes an estimate of fatality rates of different transportation modes. The safety and health impacts of shifts to specific modes are discussed below. Chu (2003 and 2006) concludes that walking has 1.7 times the fatality rate per minute of travel than traveling by motor vehicle, but the risk varies significantly by time of day and age of walker and how risk is measured. Taking into both deaths and injuries, motoring has equal or greater risk than walking. New Zealand research also indicates increased walking and cycling do not significantly increase total crash risk, and their crash rates decline with increased walking and cycling activity (Turner, Roozenburg and Francis, 2006).


Table 8            Fatalities per 100 Million Passengers in Britain (RoSPA)


Per Km

Per Trip

Per Hour





































Relative crash risk depends on the unit of measure. Faster modes rank low in crash rates per unit of distance, but not so low when measured by trips or hour of travel.




Shifting from driving to public transit tends to reduce crash risk per passenger trip because professional drivers tend to have lower crash rates, bus occupants are safer than automobile occupants, and because it reduces total vehicle traffic (Transit Evaluation). Bus passengers have about one-tenth the per-mile crash fatality rate as automobile passengers and transit-oriented communities have about a fifth of the per capita traffic fatality rate as sprawled areas (Litman 2016). Urban transit has a relatively high total fatality rate (including both occupants and other road users) per passenger mile in the U.S. due to low load factors (passengers per vehicle-mile). TDM strategies that increase transit load factors (more ridership without a comparable increase in vehicle-miles) have very small marginal crash risk, and so reduce crash rates per passenger-mile.


Figure 5          Traffic Deaths (Litman, 2004)


Per capita traffic fatalities tends to decline with increased transit ridership. These values include deaths to transit passengers, automobile passengers, and pedestrians.



Shifting from driving to transit tends to reduce annual personal travel. Transit dependent people trend to travel only about a third as many miles per year as an average motorist. Discretionary transit riders (those that have a car but ride transit for some trips) also tend to reduce their total travel when they use transit, for example, by running errands within walking distance of their worksites rather than driving to a farther destination. As a result of these indirect mileage reductions, the total safety benefits of mode shifting may be far greater than a simple mile-for-mile comparison would indicate. Per capita traffic fatalities tend to be far smaller in transit-oriented urban areas than in automobile-oriented cities (Laube 1997; Newman and Kenworthy 1999, p. 118; Page 2001; Litman 2014 and 2016), as illustrated in Figure 5.


TDM Strategies:

Transit Improvements, Shuttle Services, HOV Priority, Park & Ride, Bike/Transit Integration, Transit Oriented Development



Ridesharing reduces overall crash risk by reducing total vehicle mileage. Two people who carpool rather than drive alone bear about the same level of internal risk (assuming that their driving skills and vehicles safety do not change), but reduce risk to others by using one vehicle rather than two. Some HOV lanes have relatively high crash rates due to awkward merging conditions, and vanpools may have a relatively high rollover rate which may increase crash severity under some conditions (NHTSA, 2001).

TDM Strategies

Ridesharing, HOV Priority, Park & Ride


Nonmotorized Transport

Nonmotorized travel tends to have relatively high per-mile crash rates (Table 8), suggesting that shifting travel to these modes increases crash risk. However, total safety and health risks are lower because: 


·         Nonmotorized travel imposes minimal risk to other road users.


·         Nonmotorized trips tend to be shorter than motorized trips. A local walking trip often substitutes for a much longer automobile trip.


·         High crash and casualty rates for pedestrians and cyclists result, in part, because people with particular risk factors tend to use these modes, including children, people with disabilities and elderly people. A skilled and responsible adult who shifts from driving to nonmotorized travel is likely to experience less additional risk than these average values suggest.


·         Nonmotorized travel provides Health and Fitness benefits that can offset crash risk (BMJ, 2000; Frank and Engelke 2000; de Hartog, et al. 2010). One study found that bicycle commuters have a 40% reduction in mortality compared with people who do not cycle to work, which suggests that the incremental risks of cycling are far outweighed by health benefits, at least for experienced adult cyclists riding in a bicycle-friendly community (Anderson 2000).


·         Communities that invest in walking and cycling safety can significantly reduce crash risk (Nabors, et al. 2012). For example, between 1991 and 2000 the city of York, UK reduced total pedestrian casualties by 36%, although the portion of trips by walking increased during that time, due to traffic safety programs (WHO 2003).



Urban regions with a large portion of total travel by walking and cycling tend to have lower per capita traffic fatalities than more automobile-dependent communities, as illustrated in Figure 6. This probably reflects lower per capita vehicle traffic, greater consideration of pedestrians and cyclists in roadway design and traffic management. de Hartog, et al. (2010) used an extensive analysis of research on physical activity, air pollution and accident risk to quantify the all-cause mortality impacts of shifts from car to bicycle for short trips on a daily basis in the Netherlands. For the individuals who shift from car to bicycle the study estimates that beneficial effects of increased physical activity are substantially larger (3 – 14 months gained) than the potential mortality effect of increased inhaled air pollution doses (0.8 – 40 days lost) and the increase in traffic accidents (5 – 9 days lost). Societal benefits are even larger due to a modest reductions in air pollution and traffic accident risk. The researchers conclude that the estimated health benefits of cycling were substantially larger than the risks relative to car driving for individuals shifting mode of transport.


Figure 6          Traffic Fatalities Versus Nonmotorized Transport Rates (Newman and Jeff Kenworthy, 1999)

As nonmotorized transport increases, traffic fatalities decline.



Pedestrian and bicycle safety programs can significantly increase safety for nonmotorized travelers. Pedestrian fatalities per billion km walked are less than a tenth as high, and bicyclist fatalities are only a quarter as high, in the Netherlands and Germany as in the United States (Pucher and Dijkstra 2000). TDM strategies that promote nonmotorized through safety education and facility improvements can reduce traffic crashes.

TDM Strategies

Pedestrian and Bicycle Planning, Pedestrian Improvements, Cycling Improvements, Pedestrian and Cycling Encouragement, Universal Design, Bike/Transit Integration, Traffic Calming



Efficient Transport Pricing

More efficient road, parking, insurance and fuel pricing tends to reduce total vehicle travel, which reduces total traffic risk. Crash reductions vary depending on the type of price change, the portion of vehicle travel affected, and the quality of transport options available. If implemented to the degree justified on economic efficiency grounds (for example, cost recovery road and parking pricing), these reforms are predicted to reduce traffic casualties by 40-60% (Litman 2011).



Vehicle Mileage Reductions

As described earlier, reduced mileage tends to reduce crash rates. Studies suggest that each 1.0% reduction in vehicle mileage reduces crash costs by 1.4-1.8%, although these impacts may vary depending on the type of mileage reduced. A reduction in just freeway driving or just sober driving may cause a smaller safety benefit than if all driving declines equally. However, the empirical evidence indicates that broad reductions in driving generally do reduce overall crash rates, suggesting that a mix of low- and high-risk driving are normally reduced.

TDM Strategies

Commute Trip Reduction, Distance-Based Charges, Parking Pricing, Road Pricing, Telework, Freight Transport Management, Vehicle Restrictions and various other TDM strategies.



The central London congestion charging scheme was introduced on 17 February 2003, with the primary aim of reducing traffic congestion in and around the charging zone (London 2004). First year results indicate that the program has reduced accidents:

·         Total vehicle–kilometres reduced by 12%, car traffic reduced by 30%, crashes declined 28%.

·         Moped and motorbike travel increased 10 –15%, with 4% fewer crashes.

·         Bicycle travel increased 20%, with a 7% reduction in crashes.

·         Crashes involving pedestrians declined 6%.

·         Increased bus journey time reliability by up to 60%.

·         No evidence of any overall increase in road traffic outside the zone.

·         Subjective improvements in noise and air quality.


Pay-As-You-Drive Vehicle Insurance

Pay-As-You-Drive pricing converts vehicle insurance premiums from a fixed cost into a variable cost, so the more your drive the more you pay and the less you drive the more you save. Existing vehicle-year premiums are prorated by mileage, so insurance is priced by the vehicle-mile rather than the vehicle-year. This incorporates all existing rating factors so lower-risk motorists pay less per mile than higher-risk motorists.


This price structure gives higher-risk motorists a greater incentive to reduce their driving than lower-risk motorists. For example, a motorist who currently pays annual premiums of $375 would pay 3¢ per mile, and is likely to reduce mileage by about 6%. A higher-risk motorist who currently pays annual premiums of $1,250 would pay 10¢ per mile, and so could be expected to reduce mileage by about 18%. To the degree that insurance companies can accurately identify motorists who are higher risk per vehicle-mile, this should provide an additional reduction in crash rates. As a result, Pay-As-You-Drive Insurance is predicted to reduce mileage an average of 10% among participants, while crash costs and fatalities decline 15-20%, assuming a –1.4 to –1.8 elasticity of crash costs to vehicle mileage, increased by 10-15% due to the additional incentive for higher-risk motorists to reduce their mileage.


As described earlier, it would be poor public policy for vehicle insurance to totally compensate all crash damages, since this may reduce the incentive for road users to be cautious and avoid crashes (for example, if crash victims expected to receive a million dollar award for a broken leg, pedestrians may be less cautious when crossing the street, and drivers my be less defensive). As a result, current insurance costs are lower than total crash costs, and additional vehicle fees or taxes may be justified to reflect the full marginal crash risk imposed by each vehicle (Vickrey, 1968). Such fees or taxes would further increase road safety.

TDM Strategies

Pay-As-You-Drive Vehicle Insurance, Distance-Based Charges



Improved Automobile Availability

Some TDM strategies increase the affordability and availability of automobile travel. The safety impacts of these strategies vary depending on the travel choices that consumers would otherwise make. For example, Carsharing and Pay-As-You-Drive Insurance make occasional use of an automobile more affordable. This may increase annual vehicle-mileage by drivers who would otherwise have no car, but reduces annual vehicle-mileage by drivers who would otherwise own a personal car with unlimited-mileage insurance. In most situations these strategies result in overall mileage reductions, but the net impact will vary. Similarly, Taxi Improvements can increase motor vehicle travel by some people, but allow others to reduce or give up driving (for example, higher-risk drivers due to physical or mental disabilities may be more willing to give up driving if taxi service is good).

TDM Strategies

Carsharing, Distance-Based Charges, Taxi Improvements



Vehicle Fuel Efficiency

Some experts argue that higher fuel prices would cause motorist to drive smaller cars that provide less occupant protection, but others refute this, claiming that fuel-efficient vehicles do not necessarily provide less protection and fuel-efficient vehicles impose less risk to other road users (NRC 2001).

TDM Strategies

Fuel Price Increases, Fuel Efficiency Standards



Mobility Substitutes

Mobility substitutes include Telework and Delivery Services. The resulting mileage reductions tend to reduce crashes, although in many cases there are Rebound Effects, such as the tendency of telecommuters to make special trips for errands that they would otherwise perform while commuting, and to move farther from their worksite to less accessible, exurban locations. These rebound effects typically offset about a third of mileage reductions and associated safety benefits. For example, an employee who telecommutes three days a week would reduce commute mileage by 60%, but may drive additional miles for errands, resulting in a 40% net reduction in vehicle mileage and more modest safety benefits.

TDM Strategies

Telework, Freight Transport Management



Land Use

Land use patterns can have various traffic safety and health impacts (Land Use Impacts On Transportation). Higher density, Clustered development patterns tend to increase traffic density (vehicles per lane-mile), which tends to increase crash rates per vehicle-mile within the area. However increased density also tends to reduce per capita vehicle mileage (particularly if increased density is implemented as part of an overall Smart Growth program to improve Accessibility and encourage use of alternative modes) and it tends to reduce crash severity (due to lower traffic speeds). As a result, per capita traffic fatalities tend to decline in higher density urban areas, and increase in more Automobile Dependent, sprawled areas (Newman and Kenworthy 1999; Ewing, Schieber and Zegeer 2003 Scheiner and Holz-Rau 2011), as indicated in the figure below.


Figure 7          Annual Traffic Death Rate (Ewing, Schieber and Zegeer 2003)

The ten U.S. communities ranked least sprawled have much lower annual traffic fatality rates than the ten communities that are ranked most sprawled.



Residents of low-density suburbs have four times the risk of an injury-causing car crash than otherwise comparable residents of higher-density urban neighborhoods in the Puget Sound region, because suburban residents drive on average three times as much and twice as fast as urban dwellers (Litman and Fitzroy 2005). All told, city residents are much safer, even taking into account other risks that increase with urban living, such as pedestrian traffic injuries and homicide (Lucy 2003; Myers, et al. 2013).


Automobile-oriented transportation systems are also associated with reduced exercise and unhealthy weight gains, and TDM strategies that increase walking and cycling can provide significant health benefits (Frank and Engelke 2000). The U.S. Center for Disease Control advocates Active Community Environments, which are design features that accommodate and encourage nonmotorized transportation (Killingsworth and Lamming 2001)


This suggests that TDM strategies which create more clustered, accessible land use and more balanced transportation systems can reduce per capita crash costs and increase aerobic health.

TDM Strategies

Smart Growth, Location Efficient Development, New Urbanism, Transit Oriented Development, Access Management, Traffic Calming, Vehicle Restrictions, Carfree Planning, Least Cost Planning, Institutional Reforms.



Improved Personal Security

Some TDM strategies affect people’s exposure to personal security threats. Pedestrians, cyclists and transit riders often feel that they are exposed to greater risk off assault compared with making the same trip in a private automobile. On the other hand, increased use of these modes may increase overall personal safety, because as more responsible citizens walk, cycle and use transit, their presence on streets, in transit stations and transit vehicles tends to reduce personal security risks to others. Traveling by alternative modes can also reduce “road rage” involving drivers.


Some TDM strategies Address Security Concerns directly, and land use management strategies such as New Urbanism increase community interactions and the number of “eyes on the street”, which can increase personal security.

TDM Strategies

Address Security Concerns, Guaranteed Ride Home, Street Reclaiming, Pedestrian and Bicycle Improvements, Transit Oriented Development, New Urbanism.



Safety Education

Some TDM programs include education and marketing components that encourage safer behavior by drivers, cyclists and pedestrians. Cycling skills training is sometimes provided as part of TDM programs.

TDM Strategies

Bicycle Encouragement, TDM Marketing, Speed Reductions



Safety and Health Impacts Summary

Table 9 summarizes the road safety and public health impacts of various travel changes resulting from TDM. Some of these impacts overlap. For example, shifting travel from driving to transit usually results in an overall reduction in personal travel and an increase in walking and cycling, each of which impacts safety and health.


Table 9            TDM Safety and Health Impact Summary

Travel Change

TDM Strategies

Safety Impacts

Traffic Speed Reductions

Traffic Calming, Vehicle Restrictions

Increases safety by reducing crash frequency and severity, and reducing total vehicle mileage. Can increase nonmotorized travel.

Access Management

Access Management

Depends on details. Reduces per-mile vehicle crash rates, but can increase traffic volumes and speeds. Can support more efficient land use and mode shifts.

Time & Route Shifts

Flextime, Congestion Pricing

Mixed. Reducing congestion tends to reduce crashes but increases the severity of crashes that do occur.

Shifts to Transit

Transit Improvements, HOV Priority, Park & Ride

Increases safety due to greater safety for transit passengers and reduced vehicle traffic. Can increase safety and health if transit travel leads to reductions in total person-miles or increases walking.

Shifts to Ridesharing

Ridesharing, HOV Priority

Modest safety benefits. Increases safety due to reduced vehicle traffic.

Shifts to Nonmotorized Modes

Walking and Cycling Improvements, Traffic Calming

Mixed. Increases crash risk to participants, but reduces risk to other road users, reduces total person-miles, and improves aerobic health.

Vehicle Mileage Reductions

Various pricing, TDM programs, other TDM strategies.

Increases safety by reducing both the risk of causing a crash and being hit. Each 1.0% reduction in vehicle-miles tends to reduce crash costs by about 1.6%.

Distance-Based Insurance

PAYD Insurance, Distance-based pricing.

Large safety benefits. Reduces total traffic. Gives high-risk motorists an extra incentive to reduce mileage.

Improved Vehicle Availability

Carsharing, Taxi Improvements

May increase automobile use by some drivers, but usually reduces overall vehicle traffic.

Vehicle Fuel Efficiency

Fuel Price Increases, Fuel Efficiency Standards

Mixed. Shifts to smaller vehicles may increase risk to some occupants, but reduces risk to other road users.

Mobility Substitutes

Telework, Delivery Services

Increases safety by reducing vehicle mileage, but there are often rebound effects that offset a portion of benefits.

Land Use & Transport System Changes

Various land use management and planning reforms

Increases safety by reducing personal travel and encouraging shifts to alternative modes. Tends to increase walking and cycling, providing aerobic exercise.

Improved Personal Security

Address Security Concerns, GRH, New Urbanism

Directly improves personal security, and can reduce crashes and improve health if this supports shifts to walking, cycling and public transit.

Safety Education

Bicycle Encouragement, marketing

Can reduce bicycle crash rates. Encourages shifts to cycling.



Evaluating Traffic Safety Impacts Of Vehicle Travel Changes

Transportation improvements that make driving more convenient, cheaper or feel safer tend to increase per capita vehicle travel and crash costs (Rebound Effects). These effects do not necessarily offset all benefits, but they can reduce the net benefits and change the nature of benefits. For example, if road and vehicle safety improvements result in faster driving the benefit becomes increased mobility, not increased safety. Failing to consider these rebound effects tends to overstate the benefits of road and vehicle improvements, and understates TDM benefits. Below are three illustrations.


Highway Improvements

Roadway capacity expansion tends to increase average traffic speeds and total vehicle travel (Rebound Effects). Wider roads and other roadway design improvements tend to encourage more intensive driving and increase average traffic speeds (Noland 2003). As described earlier, traffic congestion tends to increase crash rates but reduce fatality rates since crashes occur at lower speeds. As a result, although road projects may reduce per-mile crash rates, they often increase traffic speeds and total mileage, which increases per capita fatality rates (Elvik 2001; Ginsberg, et al. 2003).


In general, narrower roads with fewer traffic lanes (e.g., a 2 or 3-lane roads instead of 4-lanes or more) are associated with significantly reduced crash risk to pedestrians than wider roads (Zegeer, et al. 2001; Dumbaugh 2005). The additional risk to pedestrians associated with multi-lane roads can be reduced with design features such as raised center meridians (which give pedestrians a safe refuge when they are halfway across the road) and Speed Reduction strategies.


Several studies indicate that common Streetscaping strategies, such as landscaping and narrowing traffic lanes, tend to increase traffic safety (Dumbaugh 2005). Annual crash rates per vehicle-mile tend to be lowest for relatively narrow (about 10-foot) lane widths, and are highest on wider, lower volume, straight streets with higher traffic speeds (Swift 2006; Zegeer, et al, 1994). Zhang and Ivan (2005) found similar results when evaluating the incidence and severity of head-on crashes. Noland and Oh (2004) found that increased number of highway lanes appears to be associated with both increased traffic-related accidents and fatalities, increased lane widths appears to be associated with increased fatalities, and increased road shoulder width appear to be associated with a decrease in accidents. One major literature review concluded that lane widths below 2.7m (8.9-ft.) should be avoided, widths above 3.3m (10.8 ft.) only marginally increase safety; widths above 3.7m (12.1 ft.) decrease safety, and widths of 3.0-3.5m (9.8-11.5 ft.) can be ambiguous to drivers overtaking cyclists (Baker 2001). Table 10 summarizes the urban road width recommendations for optimal safety. Urban streets with 24-foot curb-to-curb widths appear to have the lowest accident rates.


Table 10          Optimal Urban Traffic Lane Widths (Baker 2001)



Lane Width

(no bike lane)

Lane Width

(adjacent bike lane)

Bike Lane Width

Local Streets


2.7-3.3 m (3.0m)





2.7-3.3 m (3.0m)

³ 2.5m

³ 1.2m

Minor Arterials


3.3-3.7 m (3.5m)

³ 3.0m

³ 1.2m



3.3-3.7 m (3.5m)

³ 3.0m

³ 1.2m



Vehicle Safety Improvements

Vehicle safety features which make motorists feel more secure (seat belts, air bags, improved brakes, etc.) tend to encourage more “intensive” driving that offsets a portion of their own potential safety gains, and increases risk to other road users (Rebound Effects). As vehicles have become safer motorists tend to drive faster, follow closer, and engage in other activities, such as talking on a telephone (Wilde 1994).


Approximately one-third of potential safety increases are offset by increased driving intensity (Chirinko and Harper, 1993). For example, if air bags were predicted to prevent 3,000 vehicle occupant deaths per year if there were no change in driver behavior, only 2,000 lives would actually be saved, and risk may increase to pedestrians, cyclists and occupants of vehicles that lack such safety equipment.



Fuel Efficiency Standards

Some Energy Conservation Strategies, such as Corporate Average Fuel Efficiency standards and Feebates, cause motorists to purchase lighter, more fuel-efficient vehicles than they would otherwise choose. This tends to have various road safety impacts. Vehicle weight reductions tend to increase risk to occupants of the lighter vehicles and reducing risk to other vehicle occupants (Wenzel and Ross 2001).


In addition, lower per-mile vehicle operating costs encourages increased annual vehicle mileage. A 30% increase in vehicle fuel efficiency typically increases vehicle mileage by 10% (Transportation Elasticities). This increased mileage increases per capita crashes. This safety impact is generally ignored in the evaluation of fuel efficiency standards (Litman, 2002).




Traffic safety experts are justifiably proud of past efforts to increase vehicle safety, which has significantly reduced per-mile crash rates. When evaluated from this perspective, traffic safety efforts can be considered a major success. However, reduced crashes per vehicle-mile are largely offset by increased vehicle mileage. Per capita crash costs have declined relatively little despite significant improvements in roadway and vehicle safety, seat belt and helmet use, emergency responses and medical treatment. As a result, the United States continues to have one of the highest per capita traffic fatality rates in the world.


Crash costs are one of the largest categories of costs associated with motor vehicle use, several times greater than congestion or pollution costs. This indicates that road safety impacts should be given priority when evaluating transportation policies. For example, if a particular action reduces traffic congestion by 10% but increases traffic crashes and fatalities by 3%, it is probably not worthwhile overall: the reduce congestion is not worth the additional crash costs. On the other hand, a traffic congestion or pollution reduction strategy is far more valuable to society if it also reduces crash costs.


TDM strategies can affect safety and health in several ways, as summarized below.


·         TDM strategies that reduce total personal travel can provide large safety benefits. Each 1% reduction in motor vehicle travel typically reduces total crashes and casualties by 1.4% to 1.8%. Examples: Market Reforms and Distance-Based Charges.


·         Pay-As-You-Drive Vehicle Insurance reduces total vehicle mileage and gives higher-risk drivers an extra incentive to reduce their mileage, and so can be particularly effective at reducing road risk. Each 1% reduction in mileage due to PAYD insurance is likely to reduce crash costs by 1.5-2.0%.


·         Strategies that reduce traffic speeds and traffic conflicts can reduce per-mile crash frequency and severity. Examples: Traffic Calming and Access Management.


·         Strategies that reduce traffic congestion tend to reduce crash frequency but increase severity, because crashes occur at higher speeds. As a result, TDM strategies that shift automobile travel time, route or destination but do not reduce total vehicle travel probably do little to increase road safety. Examples: Flextime, Telework, Congestion Pricing and Parking Management.


·         Strategies that shift travel from driving to transit and ridesharing tend to provide medium to large road safety benefits. Examples: Commute Trip Reduction programs, Transit Improvements, Shuttle Services and Ridesharing.


·         Strategies that shift automobile travel to nonmotorized modes may increase per-mile risk for the people who change mode, but this can be offset by reduced risk to other road users, reduced trip length, and health benefits from aerobic exercise. Examples: Pedestrian Improvements, Bicycle Improvements, Nonmotorized Transport Encouragement and Universal Design.


·         Strategies that create more Accessible land use patterns and more balanced transportation systems may increase crash rates per lane mile (due to increased traffic density and congestion) but tend to reduce per capita fatalities and increased aerobic health. Examples: Smart Growth, Location Efficient Development, New Urbanism, Transit Oriented Development, and Clustering.


·         Strategies that limit automobile traffic in an area may increase safety if they reduce total vehicle mileage, but may reduce safety if they simply shift traffic to other roadways. Examples: Vehicle Restrictions, Carfree Planning, and Traffic Calming.


·         Some TDM strategies directly improve personal security or promote safety. Examples: Address Security Concerns, Nonmotorized Transport Encouragement, Campus Transport Management.



Transportation professionals generally consider TDM programs to be appropriate to achieve congestion and pollution reduction objectives, but overlook safety and health benefits. Yet, in many situations, safety is among the strongest justifications for implementing TDM programs, and TDM may be among the most cost effective ways to improve traffic safety.


Many North American traffic safety experts tend to favor technological solutions (e.g., wider roads, crashworthy vehicles, airbags) over behavioral solutions (seat belt use, slower speeds, reduced vehicle mileage), on the grounds that it is difficult to change motorist behavior (Gladwell 2001). However, they have been wrong. It turns out that North American consumers do value safety. Motorists respond positively to campaigns to encourage seat belt use, helmets use and sober driving, and many are willing to pay a premium for vehicle safety features. Much greater road safety gains have resulted from behavior changes such as increased seatbelt than from passive crash protection technologies.


Similarly, there is plenty of evidence that at the margin, many consumers would prefer to drive somewhat less than they do now, if given suitable incentives and convenient, safe and affordable alternatives. Safety may be one of the greatest justifications for implementing TDM strategies. When safety benefits are correctly considered, TDM solutions may receive far greater support than they do now.


Wit and Humor

A nervous passenger, about to board a small commuter plane, stops and asks the pilot, “How often do these things crash?”

“Usually only once”, responds the pilot.



Related Chapters

See Traffic Safety Strategies for information on ways to increase traffic safety, and Health and Fitness for information on strategies that increase public health through exercise. Address Security Concerns examines ways to address risks of assault and crime to people using alternative modes. For more information on transportation safety analysis see Evaluating TDM, Transportation Costs, Transportation Statistics, Evaluating Nonmotorized Transportation, and Measuring Transportation.



Examples and Case Studies

Best Practices In Road Safety Statistics and Analysis (http://ec.europa.eu/transport/roadsafety_library/publications/supreme_f7_thematic_report_statistics_and_in_depth_analysis.pdf

A study of best practices in road safety research recommends collecting the following standardized data in each jurisdiction:

Crash data

CARE database

Linking medical files with crash data

Exposure data

Population by age, gender, region

Driver population, by license type, age, gender

Road length by road category

Vehicle fleet by type, age, make, mass, EuroNcap score

Vehicle kilometres by type, month, day, hour, region, …

Fuel sales by type, month

Infrastructure data

Several network and design characteristics/SPIs (safety performance indicators)

Characteristics of people in traffic, independent of crash involvement

Alcohol and drug use of traffic participants

Speed (average, V90, variance % exceeding limit) by road type

Seatbelts/restraint systems/helmets

Daytime running lights

Trauma management: timing and quality of treatment



Safer Streets Network (ITF 2016)

The International Transport Forum (ITF) is launching the Safer City Streets project to help cities better collaborate on road safety data collection and analysis. A database for crash data is a key part of this project to will facilitate road safety performance evaluation in world cities. This program builds on experience acquired through the ITF’s permanent road safety working group, known as International Traffic Safety Data and Analysis Group (IRTAD). A detailed report, Safer City Streets Methodology for Developing the Database and Network (ITF 2016), includes general guidance on urban data collection methods, beyond just traffic safety data. 



References And Resources For More Information


John Adams (2010), “Managing Transport Risks: What Works?,” Draft for Risk Theory Handbook; at www.hamilton-baillie.co.uk/_files/_publications/45-1.pdf.


Lars Bo Andersen, et al (2000), “All-Cause Mortality Associated With Physical Activity During Leisure Time, Work, Sports and Cycling to Work,” Archives of Internal Medicine, Vol. 160, No. 11, 12 June 2000, pp. 1621-1628; at http://archinte.ama-assn.org/cgi/reprint/160/11/1621.pdf.


Association of Traffic Safety Information Professionals (ATSIP) (www.atsip.org) assists in the design, development, and use of traffic records systems to support the traffic accident reduction and prevention. The website includes links to various traffic information resources.


ATSB (2007), International Road Safety Comparisons: The 2005 Report. A Comparison of Road Safety Statistics in OECD Nations and Australia, Australian Transport Safety Bureau (www.atsb.gov.au); at www.atsb.gov.au/publications/2007/pdf/Int_comp_05.pdf.


Mike Baker (2001), Lane Width and Its Impact on Road Safety, Capacity and the Urban Environment, Presented to ITE Victoria.


Sandy Balkin and J. Keith Ord (2001), “Assessing the Impact of Speed-Limit Increases on Fatal Interstate Crashes,” Journal of Transportation and Statistics, Vol. 4, No. 1 (www.bts.gov), April 2001, pp. 1-26.


J. Ball, M. Ward, L. Thornley, and R. Quigley (2009), Applying Health Impact Assessment to Land Transport Planning, Research Report 375, New Zealand Transport Agency (www.landtransport.govt.nz); at www.landtransport.govt.nz/research/reports/375.pdf.


Judith Bell and Larry Cohen (2009), The Transportation Prescription: Bold New Ideas for Healthy, Equitable Transportation Reform in America, PolicyLink and the Prevention Institute Convergence Partnership (www.convergencepartnership.org/transportationhealthandequity).


Lawrence Blincoe, et al. (2014), Economic and Societal Impact of Motor Vehicle Crashes, 2010, Report DOT HS 812 013, National Highway Traffic Safety Administration (www-nrd.nhtsa.dot.gov); at www-nrd.nhtsa.dot.gov/Pubs/812013.pdf.


Paul J. Burke and Shuhei Nishitateno (2015), "Gasoline Prices and Road Fatalities: International Evidence,” Economic Inquiry (https://crawford.anu.edu.au/acde/publications/publish/papers/wp2014/wp_econ_2014_18.pdf).


CARB (2010/2011), Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).


G. Chi, et al. (2010). “Gasoline Prices And Their Relationship To Drunk-Driving Crashes,” Accident Analysis and Prevention, Vol. 43(1), pp. 194–203; at http://nexus.umn.edu/Papers/GasPricesAndDrunkDriving.pdf.


G. Chi, et al. (2011), A Time Geography Approach to Understanding the Impact of Gasoline Price Changes on Traffic Safety, TRB (www.trb.org); at http://nexus.umn.edu/Papers/TimeGeography.pdf.


Robert Chirinko and Edward Harper, Jr. (1993), “Buckle Up or Slow Down? New Estimates of Offsetting Behavior and their Implications for Automobile Safety Regulation,” Journal of Policy Analysis and Management, Vol. 12, No. 2, pp. 270-296; at http://econpapers.repec.org/article/wlyjpamgt/v_3a12_3ay_3a1993_3ai_3a2_3ap_3a270-296.htm.


Xuehao Chu (2003), The Fatality Risk of Walking in America: A Time-Based Comparative Approach, Walk21 Conference - Health, Equity and the Environment, Portland, Oregon (www.americawalks.org/PDF_PAPE/Chu.pdf); at www.stm.info/transportsejc2004/presentations/pdf/chu2-pres.pdf.


Community Road Accident Database (http://europa.eu.int/comm/transport/care). Detailed data on individual accidents in participating European countries.


Jeroen Johan de Hartog, Hanna Boogaard, Hans Nijland and Gerard Hoek (2010), “Do The Health Benefits Of Cycling Outweigh The Risks?,” Environmental Health Perspectives, doi:10.1289/ehp.0901747; at http://ehp03.niehs.nih.gov/article/info%3Adoi%2F10.1289%2Fehp.0901747.


Paul de Leur (2010), Collision Cost Study, Capital Region Intersection Safety Partnership (www.drivetolive.ca); at www.drivetolive.ca/Downloads/Collision_Cost_Study_Final_Report_Feb_2010.pdf.


Ann M. Dellinger and Laurie Beck (2005), “How Risky is the Commute To School? Deaths and Injuries by Transportation Mode,” TR News 237, Transportation Research Board (www.trb.org), March-April, 2005, pp. 22-24; at http://onlinepubs.trb.org/onlinepubs/trnews/trnews237.pdf.


Mark Delucchi (2000), “Environmental Externalities of Motor-Vehicle Use in the US,” Journal of Transportation Economics and Policy, Vol. 34, No. 2, May 2000, pp. 135-168.


DfT (2009), Road Safety Strategy Beyond 2010: A Scoping Study, Road Safety Research Report No. 105, Department for Transport (www.dft.gov.uk); at http://webarchive.nationalarchives.gov.uk/+/http:/www.dft.gov.uk/pgr/roadsafety/research/rsrr/theme5/rsrr105.pdf.


DfT (2011), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport (www.dft.gov.uk/webtag)

Safety Objective: www.dft.gov.uk/webtag/documents/expert/unit3.4.php

Security Objective: www.dft.gov.uk/webtag/documents/expert/unit3.4.2.php


Nicolae Duduta, Claudia Adriazola-Steil and Dario Hidalgo (2013), Saving Lives With Sustainable Transportation, EMBARQ (www.embarq.org); at http://embarq.org/sites/default/files/Saving-Lives-with-Sustainable-Transport-EMBARQ.pdf.


Nicolae Duduta, et al. (2014), Traffic Safety on Bus Priority Systems: Recommendations for Integrating Safety Into the Planning, Design, and Operations of Major Bus Routes, EMBARQ (www.embarq.org); at www.embarq.org/sites/default/files/Traffic-Safety-Bus-Priority-Corridors-BRT-EMBARQ-World-Resources-Institute.pdf.


Eric Dumbaugh (2005), “Safe Streets, Livable Streets,” Journal of the American Planning Association (www.planning.org), Vol. 71, No. 3, pp. 283-300; at www.naturewithin.info/Roadside/TransSafety_JAPA.pdf


Aaron Edlin (1998), Per-Mile Premiums for Auto Insurance, Dept. of Economics, University of California at Berkeley (http://emlab.berkeley.edu/users/edlin).


Rune Elvik (1994), “The External Costs of Traffic Injury: Definition, Estimation, and Possibilities for Internalization,” Accident Analysis and Prevention, Vol. 26, No. 6, pp. 719-732.


Rune Elvik (1994), “An Analysis of Official Economic Valuations of Traffic Accident Fatalities in 20 Motorized Countries,” Accident Analysis and Prevention, Vol. 27, No 2, 237-247.


Rune Elvik (2001), “Zero Killed in Traffic – from Vision to Implementation,” Nordic Road & Transport Research, No. 1 (www.vti.se/nordic/1-01mapp/toi1.htm).


Rune Elvik (2003), “How Would Setting Policy Priorities According to Cost-Benefit Analyses Affect the Provision of Road Safety,” Accident Analysis & Prevention, Vol. 35 (www.elsevier.com/locate/aap), pp. 557-570.


Rune Elvik and Ulf Rydningen (2002)¸ Road Safety Measures: an Inventory of Estimates of Effect, Norwegian Institute of Transport Economics, (www.toi.no), report no. 572/2001; at www.toi.no/Publikasjoner/publication.asp?ID_Publ=1001.


Rune Elvik, Anne Borger Mysen  and Truls Vaa (2004), The Handbook of Road Safety Measures, Elsevier (www.elsevier.com), ISBN: 0-08-044091-6.


Michelle Ernst and Lilly Shoup (2009), Dangerous by Design: Solving the Epidemic of Preventable Pedestrian Deaths (and Making Great Neighborhoods), Transport for America (http://t4america.org); at http://t4america.org/docs/dangerousbydesign/dangerous_by_design.pdf.


European Transport Safety Council (www.etsc.be), is an international non-governmental organisation dedicated to the reduction of the number and severity of transport crash injuries in Europe.


European Road Safety Observatory (ERSO) website (www.erso.eu) provides information on European road crash statistics and safety strategies.


Leonard Evans (2005), Traffic Safety, Science Serving Society (www.scienceservingsociety.com).


Reid Ewing (1999), Traffic Calming; State of the Practice, FHWA and ITE (available free at www.ite.org/traffic/tcstate.htm#tcsop and www.trafficcalming.org).


Reid Ewing, Richard A. Schieber, Charles V. Zegeer (2003), “Urban Sprawl As A Risk Factor In Motor Vehicle Occupant And Pedestrian Fatalities,” American Journal of Public Health, Vol. 93, No. 9 (www.ajph.org), Sept., pp. 1541-1545; at www.minority.unc.edu/sph/minconf/2004/materials/ewing.et.al.pdf.


Reid Ewing and Eric Dumbaugh (2009), “The Built Environment and Traffic Safety: A Review of Empirical Evidence,” Journal of Planning Literature, Vol. 23 No. 4, May 2009, pp. 347-367; at http://jpl.sagepub.com/cgi/content/abstract/23/4/347.


Reid Ewing and Shima Hamidi (2014), Measuring Urban Sprawl and Validating Sprawl Measures, Metropolitan Research Center at the University of Utah for the National Cancer Institute, the Brookings Institution and Smart Growth America (www.smartgrowthamerica.org); at www.arch.utah.edu/cgi-bin/wordpress-metroresearch.


FHWA (annual reports), Highway Statistics, FHWA, USDOT (www.fhwa.dot.gov/ohim).


FHWA (2004), Traffic Safety Information Systems in Europe and Australia, National Cooperative Highway Research Program (NCHRP), Panel 20-36, Federal Highway Administration (www.fhwa.dot.gov), AASHTO and Transportation Research Board (www.trb.org); at http://international.fhwa.dot.gov/tsis_04010/2004TSISReportWeb.pdf.


FHWA (2010), Transportation Planner's Safety Desk Reference, Federal Highway Administration; at http://tsp.trb.org/assets/FR1_SafetyDeskReference_FINAL.pdf.


Lawrence Frank and Peter Engelke (2000), How Land Use and Transportation Systems Impact Public Health, Active Community Environments, Georgia Institute of Technology and Center for Disease Control (www.cdc.gov/nccdphp/dnpa/aces.htm).


Lawrence Frank, Sarah Kavage and Todd Litman (2006), Promoting Public Health Through Smart Growth: Building Healthier Communities Through Transportation And Land Use Policies, Smart Growth BC (www.smartgrowth.bc.ca); at www.vtpi.org/sgbc_health.pdf.


Per E. Gårder (2004), “The Impact of Speed and Other Variables on Pedestrian Safety in Maine,” Accident Analysis & Prevention, Volume 36, Issue 4 (www.elsevier.com/locate/aap), July 2004, pp. 533-542.


Gary Ginsberg, Eli Ben-Michael, Stephen Reingold, Elaine Fletcher and Elihu Richter (2003), “How Many Shall Live? How Many Shall Die? Deaths Resulting From the Trans-Israel Highway and Alternatives: A Risk Assessment Revisited,” Earthscan Reader on World Transport Policy & Practice, Earthscan (www.earthscan.co.uk), pp. 247-258.


Malcolm Gladwell (2001), “Wrong Turn; How the Fight to make America's Highways Safer Went Off Course,” The New Yorker (www.newyorker.com), June 11, 2001, pp. 50-61.


Malcolm Gladwell (2004), “Big and Bad: The False Security of SUVs,” The New Yorker (www.newyorker.com), 12 January 2004, pp. 28-33.


Global Road Safety Partnership (www.grsproadsafety.org) is an international partnership of private companies, government agencies and research organizations working to improve road safety in developing and transition countries.


Frank Gross, Bhagwant Persaud and Craig Lyon (2010), A Guide to Developing Quality Crash Modification Factors, Federal Highway Administration; at http://safety.fhwa.dot.gov/tools/crf/resources/fhwasa10032/fhwasa10032.pdf.


Ezra Hauer (2003), The Harm Done by Tests of Significance, University of Toronto (http://members.rogers.com/hauer/Pubs/Theharmdone.pdf).


Frank Haight (1994), “Problems in Estimating Comparative Costs of Safety and Mobility,” Journal of Transport Economics and Policy, January, p. 7-30; at www.bath.ac.uk/e-journals/jtep/pdf/Volume_XXV111_No_1_7-30.pdf.


International Cooperation on Theories and Concepts in Traffic Safety (www.ictct.org) is an association that works to achieve a deeper understanding of traffic risk and opportunities for accident prevention.


ITE (2007), Desktop Reference for Crash Reduction Factors, Institute of Transportation Engineers (www.ite.org); at www.ite.org/safety/issuebriefs/Desktop%20Reference%20Complete.pdf.


ITF (2012), Pedestrian Safety, Urban Space and Health, International Transport Forum for the OECD (www.oecdbookshop.org); at http://www.oecd.org/bookshop?9789282103654.


ITF (2016), Safer City Streets: Methodology for Developing the Database and Network, International Transport Forum (www.itf-oecd.org); at www.itf-oecd.org/node/20527.


Helen James (1991), “Under-Reporting of Road Traffic Accidents,” Traffic Engineering & Control, Vol. 32, No. 12, Dec. 1991, pp. 574-583.


Peter L. Jacobsen (2003), “Safety In Numbers: More Walkers and Bicyclists, Safer Walking and Bicycling.” Injury Prevention (http://ip.bmjjournals.com), Vol. 9, pp. 205-209; at http://injuryprevention.bmj.com/cgi/content/full/9/3/205.


Mary Janke (1991), “Accidents, Mileage, and the Exaggeration of Risk,” Accident Analysis and Prevention, Vol. 23, No. 3, pp. 183-188.


Ian Johnston (2010), “Beyond ‘Best Practice’ Road Safety Thinking and Systems Management – A Case For Culture Change Research,” Safety Science, Vol. 48, pp. 1175–1181; at www.sciencedirect.com/science/article/pii/S0925753509002148.


Dewan Masud Karim (2015), Narrower Lanes, Safer Streets, CITE Annual Conference; at http://bit.ly/1GKiICo.


Richard E. Killingsworth and Jean Lamming (2001), “Development and Public Health; Could Our Development Patterns be Affecting Our Personal Health?” Urban Land, Urban Land Institute (www.uli.org), July, pp. 12-17


Elizabeth Kopits and Maureen Cropper (2003), Traffic Fatalities and Economic Growth, World Bank Research Working Paper 3035 (www.worldbank.org), at http://econ.worldbank.org/files/25935_wps3035.pdf.


Young-Jun Kweon and Kara M. Kockelman (2003), “Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models,” Accident Analysis and Prevention, Vol. 35, No. 4, pp. 313-321; at  www.ce.utexas.edu/prof/kockelman/public_html/TRB02CrashRates.pdf.


W.A. Leaf and D.F. Preusser (1998), Literature Review on Vehicle Travel Speeds and Pedestrian Injuries, National Highway Traffic Safety Administration, USDOT (www.nhtsa.gov/people/injury/research/pub/hs809012.html).


Rudolph Limpert (1994), Motor Vehicle Accident Reconstruction and Cause Analysis, Fourth Edition, Michie Company (Charlottesville).


Todd Litman (2001), Distance-Based Vehicle Insurance: Feasibility, Costs and Benefits – Comprehensive Technical Report, Victoria Transport Policy Institute (www.vtpi.org).


Todd Litman (2003), “Integrating Public Health Objectives in Transportation Decision-Making,” American Journal of Health Promotion, Vol. 18, No. 1 (www.healthpromotionjournal.com), Sept./Oct. 2003, pp. 103-108; at www.vtpi.org/AJHP-litman.pdf


Todd Litman (2004), Rail Transit In America: Comprehensive Evaluation of Benefits, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/railben.pdf.  


Todd Litman (2005), “Efficient Vehicles Versus Efficient Transportation: Comparing Transportation Energy Conservation Strategies,” Transport Policy, Volume 12, Issue 2, March, Pages 121-129; at www.vtpi.org/cafe.pdf.


Todd Litman (2005), “Terrorism, Transit and Public Safety: Evaluating the Risks,” Journal of Public Transit, Vol. 8, No. 4 (www.nctr.usf.edu/jpt/journal.htm), pp. 33-46.; at www.vtpi.org/transitrisk.pdf.


Todd Litman (2006), If Health Matters: Integrating Public Health Objectives into Transportation Decision-Making, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/health.pdf


Todd Litman (2007), Whose Roads? Evaluating Bicyclists’ and Pedestrians’ Right to Use Public Roadways, VTPI (www.vtpi.org); at www.vtpi.org/whoserd.pdf.


Todd Litman (2009), Transportation Cost and Benefit Analysis: Techniques, Estimates and Implications, Victoria Transport Policy Institute (www.vtpi.org). Includes a chapter on “Crashes, Security and Health Impacts” at www.vtpi.org/tca/tca0503.pdf.


Todd Litman (2009), “Public Transportation and Health,” in The Transportation Prescription: Bold New Ideas for Healthy, Equitable Transportation Reform in America, (Bell and Cohen eds.)  PolicyLink and the Prevention Institute Convergence Partnership (www.convergencepartnership.org/transportationhealthandequity).


Todd Litman (2009), “Transportation Policy and Injury Control,” Injury Prevention, Vol. 15, Issue 6 (http://injuryprevention.bmj.com/content/15/6/362.full); at www.vtpi.org/tpic.pdf.


Todd Litman (2010), Evaluating Public Transportation Health Benefits, American Public Transportation Association (www.apta.com); at www.vtpi.org/tran_health.pdf.


Todd Litman (2012), “Pricing for Traffic Safety: How Efficient Transport Pricing Can Reduce Roadway Crash Risks,” Transportation Research Record 2318, pp. 16-22 (www.trb.org); at www.vtpi.org/price_safe.pdf.


Todd Litman (2013), “Transportation and Public Health,” Annual Review of Public Health, Vol. Vol. 34, pp. 217-233 (www.annualreviews.org/doi/abs/10.1146/annurev-publhealth-031912-114502); draft version at www.vtpi.org/ARPH_Litman_2012.pdf.


Todd Litman (2013), Safer Than You Think! Revising the Transit Safety Narrative, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/safer.pdf


Todd Litman (2014), “How Transport Pricing Reforms Can Increase Road Safety,” Traffic Infra Tech, April-May 2014, pp. 68-71 (http://emag.trafficinfratech.com ); at www.vtpi.org/TIT-pricesafety.pdf.


Todd Litman (2014), “A New Transit Safety Narrative,” Journal of Public Transportation (www.nctr.usf.edu/category/jpt), Vol. 17, No. 4, pp. 114-135; at www.nctr.usf.edu/wp-content/uploads/2014/12/JPT17.4_Litman.pdf.


Todd Litman (2016), The Hidden Traffic Safety Solution: Public Transportation, American Public Transportation Association (www.apta.com); at www.apta.com/mediacenter/pressreleases/2016/Pages/Hidden-Traffic-Safety-Solution.aspx


Todd Litman (2017), A New Traffic Safety Paradigm, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/ntsp.pdf.


Todd Litman (2019), “Toward More Comprehensive Evaluation of Traffic Risks and Safety Strategies” Research in Transportation Business & Management (https://doi.org/10.1016/j.rtbm.2019.01.003).


Todd Litman and Steven Fitzroy (2005), Safe Travels: Evaluating Mobility Management Traffic Safety Impacts, VTPI (www.vtpi.org); at www.vtpi.org/safetrav.pdf.


London (2004), Congestion Charging: Update On Scheme Impacts And Operations, Transport for London (www.tfl.gov.uk/tfl/downloads/pdf/congestion-charging/cc-12monthson.pdf).


Gordon Lovegrove and Terek Sayed (2006), “Macro-level Collision Prediction Model For Evaluating Neighborhood Level Traffic Safety,” Canadian Journal of Civil Engineering, Vol. 33, No. 5 (http://pubs.nrc-cnrc.gc.ca/cgi-bin/rp/rp2_tocs_e?cjce_cjce5-06_33), May, pp. 609-621.


Gordon Lovegrove and Todd Litman (2008), Macrolevel Collision Prediction Models to Evaluate Road Safety Effects of Mobility Management Strategies: New Empirical Tools to Promote Sustainable Development, Transportation Research Board 87th Annual Meeting (www.trb.org); at www.vtpi.org/lovegrove_litman.pdf.


Gord Lovegrove, Clark Lim and Tarek Sayed (2010), “Community-Based, Macrolevel Collision Prediction Model Use with a Regional Transportation Plan,” Journal Of Transportation Engineering, Vol. 136, No. 2, February 2010, pp. 120-128; abstract at http://cedb.asce.org/cgi/WWWdisplay.cgi?253404.


William Lucy (2002), Danger in Exurbia: Outer Suburbs More Dangerous Than Cities, University of Virginia (www.virginia.edu); summarized in www.virginia.edu/topnews/releases2002/lucy-april-30-2002.html


William H. Lucy (2003), “Mortality Risk Associated With Leaving Home:  Recognizing the Relevance of the Built Environment,” American Journal of Public Health (www.ajph.org), Vol. 93, No. 9, September, pp. 1564-1569; at www.ajph.org/cgi/content/full/93/9/1564.


Wesley E. Marshall and Norman W. Garrick (2011), “Evidence on Why Bike-Friendly Cities Are Safer for All Road Users,” Environmental Practice, Vol. 13/1, March; at http://files.meetup.com/1468133/Evidence%20on%20Why%20Bike-Friendly.pdf.


Murray May, Paul J. Tranter and James R. Warn (2008), “Towards a Holistic Framework for Road Safety in Australia,” Journal of Transport Geography, Vol. 16 (www.elsevier.com/locate/jtrangeo), pp. 395–405. More comprehensive report published by the NMRA Road Safety Trust (www.roadsafetytrust.org.au).


Murray May, Paul J. Tranter and James R. Warn (2011), “Progressing Road Safety Through Deep Change and Transformational Leadership,” Journal of Transport Geography, Vol. 19 (www.elsevier.com/locate/jtrangeo), pp. 1423-1430; at https://blogs.otago.ac.nz/amc/files/2011/08/May-Tranter-Warn-JTG-road-safety.pdf


Ted Miller (2000), “Variations Between Countries in Values of Statistical Life,” Journal of Transport Economics and Policy, Vol. 34, No 2, 169-188.


Dinesh Mohan (2013), Safety, Sustainability and Future Urban Transport, Eicher Group (www.eicher.in); at www.eicher.in/urbanmobility.


Dinesh Mohan, Shrikant I. Bangdiwala and Andres Villaveces (2017), “Urban Street Structure and Traffic Safety,” Journal of Safety Research, Vol. 62, pp. 63–71 (www.sciencedirect.com/science/article/pii/S0022437516305357).


Christopher Murray (1996), Global Burden of Disease and Injury, Center for Population and Development Studies, Harvard University School of Public Health (www.hsph.harvard.edu/organizations/bdu).


Sage R. Myers, et al. (2013), “Safety in Numbers: Are Major Cities the Safest Places in the United States?” Annals of Emergency Medicine, Vol. 62, Is. 4, pp. 408-418.e3, American College of Emergency Physicians; at www.annemergmed.com/webfiles/images/journals/ymem/FA-5548.pdf.


Dan Nabors, et al. (2012), Bicycle Road Safety Audit Guidelines and Prompt Lists, Federal Highway Administration Office of Safety (http://safety.fhwa.dot.gov); at http://safety.fhwa.dot.gov/ped_bike/tools_solve/fhwasa12018/fhwasa12018.pdf.


NCIPC, Injury Mortality Reports (http://webappa.cdc.gov/sasweb/ncipc/mortrate10_sy.html) National Center for Injury Prevention and Control, Centers for Disease Control and Prevention.


NCSA, National Center for Statistics and Analysis, National Highway Traffic Safety Administration (www.nhtsa.dot.gov/people/ncsa) provides a wide range of analytical and statistical support for road risk research.


National Highway Traffic Safety Administration (www.nhtsa.dot.gov) provides comprehensive traffic crash data and information on safety programs.


NHTSA, Fatality Analysis Reporting System, National Highway Traffic Safety Administration (www.nhtsa.dot.gov) is a comprehensive system for collecting information on fatal crashes in the U.S.


NHTSA (2005), Motor Vehicle Traffic Crashes as a Leading Cause of Death in the U.S., 2002 – A Demographic Perspective, National Center for Statistics and Analysis; National Highway Traffic Safety Administration, DOT HS 809 843 (www-nrd.nhtsa.dot.gov/pdf/nrd-30/NCSA/Rpts/2005/809843.pdf).


Peter Newman and Jeff Kenworthy (1999), Sustainability and Cities: Overcoming Automobile Dependence, Island Press (www.islandpress.org).


Robert Noland (2003), “Traffic Fatalities and Injuries: The Effects of Changes in Infrastructure and Other Trends,” Journal of Accident Prevention and Analysis, Vol. 35, pp. 599-611; at www.bikewalktwincities.org/sites/default/files/Noland-Traffic_Fatalities_and_Injuries.pdf.


Robert B. Noland and Lyoong Oh (2004), “The Effect of Infrastructure and Demographic Change on Traffic-Related Fatalities and Crashes: a Case Study of Illinois County-Level Data,” Accident Analysis & Prevention, Volume 36, Issue 4 (www.elsevier.com/locate/aap), July 2004, pp. 525-532; at  www.cts.cv.imperial.ac.uk/documents/publications/iccts00254.pdf.


Noxon Associates (2008), The Case for TDM In Canada: Transportation Demand Management Initiatives and Their Benefits – A Handbook for Practitioners, Association for Commuter Transportation of Canada (www.actcanada.com); at www.actcanada.com/EN/Downloads/Case%20for%20TDM%20in%20Canada%20FINAL%20October%202008.pdf.


NYC (2010), The New York City Pedestrian Safety Study & Action Plan, New York City Department of Transportation (www.nyc.gov); at www.nyc.gov/html/dot/downloads/pdf/nyc_ped_safety_study_action_plan.pdf.


OECD, International Road Traffic and Accident Database (www.bast.de/irtad), is maintained by the Organization for Economic Cooperation and Development.


Yves Page (2001), “A Statistical Model to Compare Road Mortality in OECD Countries,” Accident Analysis and Prevention, Vol. 33 (www.elsevier.com/locate/aap), pp. 371-385.


PIARC (2015), Road Safety Manual: A Manual for Practitioners And Decision Makers On Implementing Safe System Infrastructure, World Road Association (http://roadsafety.piarc.org); at http://roadsafety.piarc.org/en.


John Pucher and Lewis Dijkstra (2000), “Making Walking and Cycling Safer: Lessons from Europe,” Transportation Quarterly, Vol. 54, No. 3, Summer; at www.vtpi.org/AJPHpucher.pdf.


Francesca Racioppi, Lars Eriksson, Claes Tingvall and Andres Villaveces (2004), Preventing Road Traffic Injury: A Public Health Perspective For Europe, World Health Organization, Regional Office for Europe (www.euro.who.int/document/E82659.pdf).


Elihu D. Richter, Tamar Berman, Lee Friedman and Gerald Ben-David (2006), “Speed, Road Injury and Public Health,” Annual Review of Public Health, Vol. 27 (http://arjournals.annualreviews.org, April, pp. 125-152.


Dorothy Robinson (2005), “Safety in Numbers in Australia: More Walkers and Bicyclists, Safer Walking and Bicycling,” Health Promotion Journal of Australia, Vol. 16, No. 1 (www.healthpromotion.org.au), April 2005, pp. 47-51.


Safety Conscious Planning (www.fhwa.dot.gov/planning/SCP), is a U.S. Federal Highway Administration website providing information on ways to incorporate traffic safety into transportation planning.


Joachim Scheiner and Christian Holz-Rau (2011), “A Residential Location Approach To Traffic Safety: Two Case Studies From Germany,” Accident Analysis & Prevention, Vol. 43, Is. 1, January, pp. 307-322; summary at www.sciencedirect.com/science/article/pii/S0001457510002629.


Maria SeguiGomez, et al. (2011), “Exposure to Traffic and Risk of Hospitalization Due to Injuries,” Journal of Risk Analysis, Vol. 31, No. 3, pp. 466-474 (DOI: 10.1111/j.1539-6924.2010.01509.x); at http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2010.01509.x/abstract.


D. Shefer and P. Rietvald (1997), “Congestion and Safety on Highways: Towards an Analytical Model,” Urban Studies, Vol. 34, No. 4, pp. 679-692.


Michael Sivak (2008), Is the U.S. on the Path to the Lowest Motor Vehicle Fatalities in Decades?, Report UMTRI-2008-39, University of Michigan Transportation Research Institute (www.umtri.umich.edu); at http://deepblue.lib.umich.edu/bitstream/2027.42/60424/1/100969.pdf.


Michael Sivak and Brandon Schoettle (2014), Mortality from Road Crashes in 193 Countries: A Comparison with Other Leading Causes of Death, University of Michigan Transportation Research Institute (www.umich.edu/~umtriswt); at http://deepblue.lib.umich.edu/bitstream/handle/2027.42/102731/102989.pdf.


Michael Sorensen and Marjan Mosslemi (2009), Subjective and Objective Safety - The Effect of Road Safety Measures on Subjective Safety Among Vulnerable Road Users, Institute of Transport Economics (TØI) of the Norwegian Centre for Transport Research (www.toi.no); at www.toi.no/getfile.php/Publikasjoner/T%D8I%20rapporter/2009/1009-2009/1009-2009-nett.pdf.


Jim P. Stimpson, et al. (2014), “Share of Mass Transit Miles Traveled and Reduced Motor Vehicle Fatalities in Major Cities of the United States,” Journal of Urban Health: Bulletin of the New York Academy of Medicine, (doi:10.1007/s11524-014-9880-9); at http://link.springer.com/article/10.1007%2Fs11524-014-9880-9.


Jack Stuster and Zail Coffman (1998), Synthesis of Safety Research Related to Speed and Speed Limits, Federal Highway Administration, FHWA-RD-98-154 (www.tfhrc.gov/safety/speed/speed.htm).


SUPREME (2005), “Statistics & In Depth Analysis,” Best Practices In Road Safety, Summary And Publication Of Best Practices In Road Safety In The Member States, European Commission (http://ec.europa.eu); at http://ec.europa.eu/transport/supreme/index_en.htm.


Peter Swift, Dan Painter and Matthew Goldstein (2006), Residential Street Typology and Injury Accident Frequency, Swift and Associates, originally presented at the Congress for the New Urbanism; at http://massengale.typepad.com/venustas/files/SwiftSafetyStudy.pdf.


Traffic Safety Center (www.tsc.berkeley.edu) is a multi-disciplinary research center involving transportation and public health professionals that promotes traffic safety.


TrafficSTATS (www.aaafoundation.org/trafficSTATS) provides an interactive tool for providing information on traffic risk (by vehicle-mile, vehicle-trip and minute of travel) for different transport modes, travel conditions, demographic groups and various other parameters. This project is a joint venture between Carnegie Mellon University and the AAA Foundation for Traffic Safety.


Paul Joseph Tranter (2010), “Speed Kills: The Complex Links Between Transport, Lack of Time and Urban Health,” Journal of Urban Health, Vol. 87, No. 2, doi:10.1007/s11524-009-9433-9; at www.springerlink.com/content/v5206257222v6h8v.


S.A. Turner, A. P. Roozenburg and T. Francis (2006), Predicting Accident Rates for Cyclists and Pedestrians, Land Transport New Zealand Research Report 289 (www.ltsa.govt.nz); at www.ltsa.govt.nz/research/reports/289.pdf.


T.Y. Lin International (2012), Sharing the Road: Optimizing Pedestrian and Bicycle Safety and Vehicle Mobility, Michigan Department of Transportation (www.michigan.gov/mdot); at www.michigan.gov/mdot/0,4616,7-151-9622_11045_24249-279311--,00.html


T.Y. Lin International (2012), Best Design Practices for Walking and Bicycling in Michigan, Michigan Department of Transportation (www.michigan.gov/mdot) at www.michigan.gov/documents/mdot/MDOT_Research_Report_RC1572_Part6_387521_7.pdf.


United Nations Road Safety Collaboration (www.who.int/roadsafety) is a World Health Organization effort to support road safety programs throughout the world.


Tom Vanderbilt (2008), Traffic: Why We Drive The Way We Do (And What It Says About Us), Vintage (www.howwedrive.com).


William Vickrey (1968), “Automobile Accidents, Tort Law, Externalities, and Insurance: An Economist’s Critique,” Law and Contemporary Problems, Vol. 33, pp. 464-487; at www.vtpi.org/vic_acc.pdf.


Vision Zero Network (https://visionzeronetwork.org) helps communities identify and implement the most effective safe mobility strategies.


Hans-Joachim Vollpracht (2010), “They Call Them Coffin Roads,” Routes-Roads, N° 347, World Road Association (www.piarc.org); at www.vtpi.org/Vollpracht.pdf.


Jing-Shiarn Wang, Ronald R. Knipling and Lawrence J. Blincoe (1999), “The Dimensions of Motor Vehicle Crash Risk, Journal of Transportation and Statistics (www.bts.gov), Vol. 2, No. 1, May 1999, pp. 19-43.


Kevin Watkins (2012), Safe and Sustainable Roads: The Case for a Sustainable Development Goal, The Campaign For Global Road Safety (www.makeroadssafe.org); at www.makeroadssafe.org/publications/Documents/Sustainable_Transport_Goal_report.pdf.


Ben Welle, et al. (2015), Cities Safer by Design: Urban Design Recommendations for Healthier Cities, Fewer Traffic Fatalities, World Resources Institute Ross Cener of Sustainable Cities (www.wricities.org); at www.wricities.org/research/publication/cities-safer-design.


Tom Wenzel and Marc Ross (2002), “Are SUV’s Really Safer than Cars?”, Access, Number 21, University of California Transportation Center (www.uctc.net), Fall 2002, p. 2-7. 


Vicky Feng Wei and Gord Lovegrove (2010), “Sustainable Road Safety: A New (?) Neighbourhood Road Pattern That Saves VRU (Vulnerable Road Users) Lives,” Accident Analysis & Prevention (www.sciencedirect.com/science/journal/00014575).


WHO, Adrian Davis Editor (2003), A Physically Active Life Through Everyday Transport: With A Special Focus On Children And Older People And Examples And Approaches From Europe, World Health Organization, Regional Office for Europe (www.euro.who.int/document/e75662.pdf).


WHO (2004), World Report on Road Traffic Injury Prevention: Special Report for World Health Day on Road Safety, World Health Organization (www.who.int); at www.who.int/violence_injury_prevention/publications/road_traffic/world_report/en/index.html.


WHO (2011), Health Co-Benefits of Climate Change Mitigation - Transport Sector: Health in the Green Economy, Health Impact Assessment, World Health Organization (www.who.int/hia); at www.who.int/hia/examples/trspt_comms/transport_sector_health_co-benefits_climate_change_mitigation/en/index.html.


WHO (2013), Pedestrian Safety: A Road Safety Manual for Decision-Makers and Practitioners, World Health Organization (www.who.int); at http://who.int/roadsafety/projects/manuals/pedestrian/en/index.html.


WHO (2013), Global Status Report On Road Safety 2013, World Health Organization (www.who.int); at www.who.int/violence_injury_prevention/road_safety_status/2013/en.


Gerald Wilde (1994), Target Risk, PDE Publications (http://psyc.queensu.ca/target).


Eugene M. Wilson and Martin E. Lipinski (2004), Road Safety Audits: A Synthesis of Highway Practice, National Cooperative Highway Research Program (NCHRP) Synthesis 336: Road Safety Audits (http://trb.org/publications/nchrp/nchrp_syn_336.pdf).


Charles V. Zegeer, Richard Stewart, Forrest Council and Timothy R. Neuman (1994), “Accident Relationships of Roadway Width on Low-Volume Roads,” Transportation Research Record 1445, TRB (www.trb.org), pp. 160-168.


Zegeer, et al. (2001), Safety Effects of Marked vs. Unmarked Crosswalks, University of North Carolina Highway Safety Research Center, FHWA (www.walkinginfo.org/rd/devices.htm).


Zegeer, et al. (2001), Identification of Severe Crash Factors and Countermeasures in North Carolina, North Carolina Department of Transportation (www.ncdot.org).


Charles Zegeer, et al. (2010), Pedestrian Safety Strategic Plan: Recommendations for Research and Product Development, Federal Highway Administration Office of Safety (http://safety.fhwa.dot.gov); at http://safety.fhwa.dot.gov/ped_bike/pssp/fhwasaxxxxx/fhwasaxxxxx.pdf.


C. Zhang and John Ivan (2005), “Effects of Geometric Characteristics on Head-on Crash Incidence on Two-lane Roads in Connecticut,” Transportation Research Record 1908, TRB (www.trb.org), pp. 159-164.


Min Zhou and Virginia Sisiopiku (1997), “On the Relationship Between Volume to Capacity Ratios in Accident Rates,” Transportation Research Record 1581, TRB (www.trb.org), pp. 47-52.

This Encyclopedia is produced by the Victoria Transport Policy Institute to help improve understanding of Transportation Demand Management. It is an ongoing project. Please send us your comments and suggestions for improvement.




Encyclopedia Homepage

Send Comments


Victoria Transport Policy Institute

www.vtpi.org       info@vtpi.org

1250 Rudlin Street, Victoria, BC,  V8V 3R7,  CANADA

Phone 250-360-1560

“Efficiency - Equity - Clarity”