Transportation Demand
Evaluating The Amount And Type Of Travel People Would Choose Under Specific Conditions
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Victoria Transport Policy Institute
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Updated 29 May 2015
This chapter discusses concepts related to Transport Demand, which refers to the amount and type of travel people would choose under specific price and service quality conditions.
Transportation Demand refers to the amount and type of travel people would choose under specific conditions, taking account factors such as the quality of transport options available and their prices. Understanding demand is important for Transport Planning in general and is particularly important Transportation Demand Management, which includes various strategies that influence travel behavior.
Many factors can affect travel demand, including demographics, the quality of facilities, the quality and price of alternatives, and land use patterns, as summarized in Table 1. Changes to these factors, due to trends or by design, can affect travel activity and therefore costs and problems such as congestion, accidents and pollution emissions.
Table 1 Factors That Affect Transport Demand
|
Demographics |
Economics |
Prices |
Transport Options |
Service Quality |
Land Use |
|
Number of people (residents, employees and visitors).
Incomes
Age/lifecycle
Lifestyles
Preferences |
Number of jobs
Incomes
Business activity
Freight transport
Tourist activity
|
Fuel prices and taxes
Vehicle taxes & fees
Road tolls
Parking fees
Vehicle insurance
Public transport fares |
Walking
Cycling
Public transit
Ridesharing
Automobile
Taxi services
Telework
Delivery services |
Relative speed and delay
Reliability
Comfort
Safety and security
Waiting conditions
Parking conditions
User information
Social status |
Density
Mix
Walkability
Connectivity
Transit service proximity
Roadway design
|
This table indicates various factors that affect transport demand, which should be considered in transport planning and modeling, and can be used to manage demand.
For example, rather than saying that “Vehicle travel is projected to increase by 20% during the next decade” it would be more accurate to say that “Vehicle travel is projected to increase by 20% during the next decade if current trends continue, or 10% if the community invests more in alternative modes, or not at all if the community also implements smart growth land use policies, and is projected to decline 10% if additional TDM strategies are implemented, such as efficient road and parking pricing.” This highlights the point that current policy and planning decisions can affect future travel activity.
Automobile, rail transit and air travel demand tend to increase with incomes, particularly as household shift from low to medium incomes. At high incomes, automobile travel tends to level off, and the point at which it peaks depend on factors such as the quality of alternative modes and land use policies. For example, U.S. consumers drive about twice as much in the U.S. as in other wealthy countries (Litman 2006b; Ecola, et al. 2014). Demand for walking, cycling and bus transit varies significantly depending on quality: these modes are often used by lower-income travelers and so are considered inferior goods, but in some situations they are also used significantly by higher-income travelers (APTA 2007). For example, high income neighborhoods often have high walking and cycling mode shares, and some express commuter bus services carry relatively higher-income commuters.
Table 2 Demand Characteristics By Transit Mode (CTS 2009)
|
Transit Service |
Definition |
Type of Rider |
How Transit is Accessed |
Trip Characteristics |
|
Light-Rail Transit |
Hiawatha Line from downtown Minneapolis to its southern suburbs |
Mostly (62%) choice |
Balanced between bus, walking, and park and ride
|
Home locations spread throughout the region; the average rider lives more than three miles from the line. |
|
Express Bus
|
Connects suburban areas directly to downtowns |
Primarily choice (84%)
|
About half park-and-ride (48%)
|
Home locations clustered at the line origin
|
|
Premium Express Bus |
Express routes with coach buses
|
Almost exclusively choice (96%)
|
Mostly park and ride (62%)
|
Home locations clustered at the line origin |
|
Local Bus |
Serves urban and suburban areas with frequent stops |
Mostly captive (52%)
|
Nearly all bus or walk (90%)
|
Home locations scattered along route; most riders live within a mile of the bus line |
Rail transit tends to attract more “choice” riders (discretionary transit users who could drive).
Described differently, as incomes increase consumers tend to be more sensitive to service quality than price, so higher quality services are justified to meet consumer demands. This can include a diverse range of factors including the ease of accessing information; walking and waiting conditions; station and vehicle cleanliness and comfort; amenities such as the availability of washrooms, refreshments and Internet connections; and the respect shown customers during interactions with service providers. For example, to attract higher income travelers, public transit services should be modeled after first-class rather than economy airline travel.
Similarly, as incomes increase, demand for non-transportation goods also tends to increase, including some that are in conflict with motor vehicle travel, such as reduced commuting time and stress, environmental quality (including both the built environment – towns and cities – and the natural environment), health and safety. As a result, as people become wealthier, many choose to live in more accessible, multi-modal communities so they can reduce their vehicle travel and rely more on alternative modes.
An important planning issue is the degree to which the transport system responds to consumer demands. For example, high automobile travel mode split may results from:
· Automobile travel superior performance. Consumers have viable options (they could walk, bicycle and use public transit) but prefer driving for most trips.
· Automobile travel prestige. Consumers have viable options but are often embarrassed to use them, and so choose driving for most trips.
· Inadequate alternatives. Distorted planning practices have reduced the quantity and quality of alternative modes, so walking, cycling and public transit are unavailable even when they are more cost effective than existing alternatives or consumers would willingly pay marginal costs.
· Miss-pricing. Since most vehicle costs are fixed or external, once consumers purchase an automobile they feel that they should use it, in order to get their money’s worth. As a result, consumers drive more and use alternatives less than is optional overall. Described differently, consumers lack efficient pricing options, such as unbundled parking and distance-based insurance.
It is likely that all four factors contribute to high levels of automobile travel in some situations. To the degree that automobile travel offers true superior performance, an automobile-dependent transportation system responds to consumer demands. However, to the degree that other factors (prestige, inadequate alternatives, mis-pricing) contribute to high automobile travel mode split, the resulting travel patterns are not optimal; consumers are forced to drive more than they actually want and are unable to use preferred alternatives, due in part to inadequate options.
The following trends are expected to change future travel demands (Litman 2006b; Ecola, et al. 2014):
These trends tend to reduce motor vehicle travel demand (or at least, growth in demand) and increase demand for alternative modes. This is not to suggest that automobile travel will disappear, but at the margin (compared with current travel patterns) many people would probably prefer to drive somewhat less and rely more on alternative modes, provided that they are convenient, comfortable, safe and affordable. Exactly how much travel behavior actually changes depend on factors such as the quality of alternative modes, Pricing and Land Use Factors. Described differently, as people become wealthier and their basic mobility needs are satisfied they become increasingly responsive to qualitative factors such as comfort and health concerns, and will choose transport options and home locations that respond to their preferences. For example, many wealthy people now choose homes in urban neighborhoods because they prefer being able to walk, bike and use high quality public transit for a portion of trips, rather than relying entirely on automobile transport. These are all aspects of transport demand.
Travel Demand Models can be used to predict how these factors affect travel behavior. Conventional models tend to focus on certain factors (demographics, vehicle operating costs, travel speeds, public transit fares) and overlook other factors, particularly qualitative factors related to travel comfort and land use conditions. Travel model improvements can incorporate more of these factors are needed for TDM planning.
Conventional planning tends to consider a relatively limited set of demand variables, and so often fails to convey to decision-makers the full range of options that could be used to affect demand and solve problems such as congestion, accidents and pollution. For example, a planner might say that, since area population is growing 5% annually vehicle travel demand is projected to increase at the same rate so roadway capacity must be expanded to satisfy people’s need and avoid congestion problems. This assumes that demand is a fixed value (an inflexible number of vehicle-kilometers per capita). More detailed demand analysis can give decision-makers a wider range of options for solving programs. For example:
· Conventional evaluation tends to focus on factors that are easier to quantify, such as travel speed and price, and gives less consideration to qualitative factors such as convenience, comfort and prestige. This tends to favor highway expansion and fuel subsidies over improvements in walking and cycling conditions, more convenient public transit user information, or more comfortable transit stations and vehicles.
· Conventional planning tends to accept per capita vehicle ownership and travel demand as relatively inflexible values, implying that parking facilities and roadways should be expanded as needed to meet consumers’ demands and avoid congestion.
· If demand is tested using cost-recovery Pricing (that is, the analysis investigates whether users would directly pay roadway expansion costs) demand often declines: motorists want additional roadway capacity only if it is financed indirectly, but not if they must pay for it directly. This indicates that roadway expansion project are economically inefficient and would reduce consumer welfare.
· Travel demand analysis should consider interactive effects of all modes. In some situations, improvements to alternative modes (such as rideshare programs or better public transit service) can reduce the point of Congestion Equilibrium (that is, reduce the level of congestion at which travelers shift from driving to alternative modes).
· If planners consider land use impacts on travel demand (for example, automobile-oriented development increases automobile travel demand, while transit-oriented development increases transit demand), Smart Growth strategies, such as more Transit-Oriented Development, can be considered as possible solutions to transport problems.
Improved demand analysis considers a wide range of factors that influence travel activity. This is important for Transportation Demand Management planning, which requires information on factors that influence transport activities, such as:
|
A vision without a plan is just a dream. A plan without a vision is just drudgery. But a vision with a plan can change the world. —Anonymous |
Planners, public officials and other stakeholders should become familiar with the concept of travel demand and the wide range of factors that can affect it. The Travel Model Improvement Program (http://tmip.fhwa.dot.gov) and similar resources can provide guidance on ways to effectively incorporate more factors when modeling demand.
The UK Department For Transport provides detailed guidance on methods for modeling the effects of transportation system changes, including:
· Methods of estimating changes in road traffic congestion as a result of a public transport programs.
· Advice on the development of road traffic assignment models, how to achieve convergence of these models.
· Development of public transport passenger assignment models.
· Assignment methods.
· Generalised cost analysis.
· Convergence and validation of public transport passenger assignment models.
· Design and conduct of travel demand surveys required for public transport model development.
· Creation of matrices of public transport passenger trips.
· Responses of public transport operators to changes in demand.
Hensher (2002) developed an integrated urban passenger transport model system for evaluating the travel and environmental impacts of various policy instruments, such as road, parking and fuel pricing; incentives to purchase more efficient and alternative fuel vehicles; improvements to alternative modes; and smart growth land use policies. The model system has four integrated modules defining household location and automobile choices, commuter workplace and commuting travel choices, non-commuting travel activity, and employment location. The demand model system, estimated as a set of discrete and continuous choice models, is combined with a set of equilibrating criteria in each of the location, automobile and commuting markets to predict overall demand for passenger travel in various socio-economic segments, automobile classes and geographic locations. The system is applyed to Perth (Western Australia) to evaluate policy impacts on greenhouse gas emissions. The model system is embedded within a decision support system to make it an attractive of tool for practitioners.
In a detailed baseline survey of adult residents in five U.S. communities performed before major non-motorized improvements were implemented, Krizek, et al. (2007) found that on a given day, about 2% to 4% of adults in the surveyed communities bicycle for transportation, while about 15% to 35% walk. The average daily distance for cyclists is about 5 to 8 miles; for walkers it is 1.5 to 2 miles. About 30% to 40% of bicycle or walking commute trips, and about 95% of non-motorized trips to other destinations, would have been made by driving. They estimate that walking and cycling reduce approximately 0.25 to 0.75 mile of daily driving per adult resident, or 1-4% of total automobile travel. Some of this motorized travel would be ridesharing, in which passengers use an otherwise empty seat in a vehicle that would make the trip anyway, but others generate additional vehicle travel, including some chauffeured trip in which a driver makes a special trip to carry a passenger, which often generates an empty return trip.
The UK National Transport Model (NTM) provides a systematic means of comparing the national consequences of alternative national transport policies or widely-applied local transport policies. It includes detailed data on motor vehicle ownership and travel demand.
A study by Thompson, et al. (2012) examined the factors underlying transit demand in the multi-destination, integrated bus and rail transit network for Atlanta, Georgia. Using data obtained from the 2000 Census, coupled with data obtained from local and regional organizations in the Atlanta metropolitan area, the researchers estimated several statistical models that explain the pattern of transit commute trips across the Atlanta metropolitan area. The models show that bus riders and rail riders are different, with bus riders exhibiting more transit-dependent characteristics and rail riders more choice rider characteristics. However, both types of riders value many of the same attributes of transit service quality (including shorter access and egress times and more direct trips) and their use of transit is influenced by many of the same variables (including population and employment). At the same time, the factors that influence transit demand vary depending on the type of travel destination the rider wishes to reach, including whether it is the central business district (CBD) or a more auto-oriented, suburban destination.
The results suggest that more direct transit connections to dispersed employment centers, and easier transfers to access such destinations, will lead to higher levels of transit use for both transit-dependent and choice riders. The results also show that the CBD remains an important transit destination for rail riders but not for their bus rider counterparts. Certain types of transit-oriented development (TOD) also serve as significant producers and attractors of rail transit trips.
Models are available which can predict the travel impacts of a specific Commute Trip Reduction program, taking into account the type of program and worksite. These include the TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) Model (www.nctr.usf.edu/abstracts/abs77704.htm), the CUTR_AVR Model (www.cutr.usf.edu/tdm/download.htm), the Business Benefits Calculator (BBC) (www.commuterchoice.gov) and the Commuter Choice Decision Support Tool (www.ops.fhwa.dot.gov/PrimerDSS/index.htm). See Transportation Elasticities for information on the travel impacts that result from various price changes.
A stated preference survey calculated the effects of changes in travel price (parking and transit fares) and time on commutes at the University of Laval, Quebec. It found that:
Combining these strategies increased the effects. For example, if public transit becomes free and the parking cost is increased 60%, automobile trips would decline 42%, which is more than the sum of the effects of each measure taken separately).
Marcus Robert used discrete choice models to predict how people respond to various travel options and incentives, such as employers who encourage Telework and Ridesharing. The results were then incorporated into a traffic network equilibrium model, which quantified how changes in travel behavior affect road traffic, and traffic congestion, emissions, accidents and travel times.
Johnston (2008) describes development of a statewide integrated transportation/land use urban growth model that can be used to evaluate major transportation scenarios in California, such as freeway widenings and high speed rail, and transport and land use policies intended to provide for more-affordable housing accessible to jobs, widespread habitat protection, and strong reductions in greenhouse gases. This model provides various performance measures, including travel activity, economic welfare and equity, rents paid, energy use, greenhouse gas emissions, vehicular air pollution, and habitat loss. The results are structured to reflect recent advances in the theories of well-being for persons and for nations.
The report, Estimating Bicycling and Walking for Planning and Project Development: A Guidebook (Kuzmyak, et al. 2014) describes practical methods and tools for estimating bicycling and walking demand as part of regional-, corridor-, or project-level analyses. These methods are sensitive to key planning factors, including bicycle and pedestrian infrastructure, land use and urban design, topography, and sociodemographic characteristics. The planning tools presented include some entirely new methods as well as some existing methods found to have useful properties for particular applications. These tools take advantage of existing data and the capabilities presented in GIS methods to create realistic measures of accessibility which are a critical determinant of bicycle, pedestrian, and even transit mode choice. This information should be of considerable value to transportation practitioners either directly interested in forecasting bicycling or walking activity levels or accounting for the impact of bicycle or pedestrian activity in support of broader transportation and land use planning issues
The Oregon Modeling Improvement Program (OMIP) is developing a Transportation and Land Use Model Integration Program (TLUMIP), an integrated transportation, land use and economic model for use in transportation planning and policy analyses at the regional and statewide levels. The first generation of the model, called Oregon1, has been successfully applied to several complex policy issues. Using information gained from these initial applications, Oregon2 is significantly refining and expanding elements of the program in a state-of-the-art modeling framework. This framework covers Oregon’s 36 counties and parts of adjoining states. It operates at various levels of geography, including a 30-meter grid of study area land use.
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PROSPECTS (2003), Transport Strategy: A Decisionmakers Guidebook, Konsult, Institute for Transport Studies, University of Leeds (www.konsult.leeds.ac.uk); at www.konsult.leeds.ac.uk/public/level1/sec00/index.htm; originally published as, Developing Sustainable Urban Land Use and Transport Strategies: A Methodological Guidebook; at www.infra.kth.se/courses/1H1402/Litteratur/pr_del14mg.pdf.
Robert Puentes (2008), The Road…Less Traveled: An Analysis of Vehicle Miles Traveled Trends in the U.S., Brooking Institution (www.brookings.edu); at www.brookings.edu/reports/2008/1216_transportation_tomer_puentes.aspx?emc=lm&m=220694&l=17&v=39243.
Michael Sivak (2013), Has Motorization in the U.S. Peaked?, University of Michigan, Transportation Research Institute (www.umich.edu/~umtriswt); at http://deepblue.lib.umich.edu/bitstream/handle/2027.42/98098/102947.pdf. This is the first in a series of reports which examine various factors that are contributing to peaking motor vehicle travel.
Peter R. Stopher and Stephen P. Greaves (2007), “Household Travel Surveys: Where Are We Going?,” Transportation Research A, Vol. 41, Issue 5 (www.elsevier.com/locate/tra), June 2007, pp. 367-381.
SUMMA (2003), Fast Simple Model, SUMMA (Sustainable Mobility, Policy Measures and Assessment) (www.summa-eu.org). This is a model for operationalizing the concept of sustainable transportation by predicting the impacts of various policies and programs.
Gregory Thompson, et al. (2012), Understanding Transit Ridership Demand For A Multi-Destination, Multimodal Transit Network In An American Metropolitan Area: Lessons For Increasing Choice Ridership While Maintaining Transit Dependent Ridership, Mineta Transportation Institute (www.transweb.sjsu.edu); at www.transweb.sjsu.edu/PDFs/research/1003-multi-destination-multimodal-metropolitan-area-transit-riders.pdf.
Toolbox for Regional Policy Analysis Website (www.fhwa.dot.gov/planning/toolbox/index.htm) by the US Federal Highway Administration, describes analytical methods for evaluating regional economic, social and environmental impacts of various transportation and land use policies.
Traffic Analysis Tools Website (www.ops.fhwa.dot.gov/trafficanalysistools/type_tools.htm), Office of Operations, Federal Highway Administration.
Travel Model Improvement Program (http://tmip.fhwa.dot.gov) is a U.S. Department of Transportation program to support research and information sharing for improving travel models.
TRICS (www.trics.org) is a consortium of UK regional councils to collect and distribute trip generation data based on thousands of transport surveys. This provides convenient and accurate information on the trip generation of various types of development, and the effectiveness of various mobility management strategies.
TRB (2007), Metropolitan Travel Forecasting: Current Practice and Future Direction, Special Report 288, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/sr/sr288.pdf.
TRL (2004), The Demand for Public Transit: A Practical Guide, Transportation Research Laboratory, Report TRL 593 (www.trl.co.uk); at www.demandforpublictransport.co.uk.
USEPA (2002), Smart Growth Index (SGI) Model, U.S. Environmental Protection Agency (www.epa.gov/livablecommunities/topics/sg_index.htm). For technical information see Criterion, Smart Growth Index Indicator Dictionary, U.S. Environmental Protection Agency (www.epa.gov/smartgrowth/pdf/4_Indicator_Dictionary_026.pdf).
USEPA (2005), Commuter Model, U.S. Environmental Protection Agency (www.epa.gov/oms/stateresources/policy/pag_transp.htm).
USF (2006), TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) Model, developed by the University of South Florida (www.nctr.usf.edu) evaluates the travel impacts, benefits and costs of various commute trip reduction programs and other mobility management strategies; at www.nctr.usf.edu/abstracts/abs77704.htm.
Clark Williams-Derry (2011), Dude, Where Are My Cars? Sightline Institute (www.sightline.org); at http://daily.sightline.org/blog_series/dude-where-are-my-cars.
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.
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