How Land Use Patterns Affect Travel Behavior
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Victoria Transport
Policy Institute
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Updated
August 27, 2007
This chapter describes how land use factors affect travel behavior. These factors include density, mix, roadway connectivity, parking facility management, and site design. This information is useful for evaluating how land use management strategies such as Smart Growth, New Urbanism and Access Management can help achieve transport planning objectives. For more information see the report “Land Use Impacts On Travel Behavior” at www.vtpi.org/landtravel.pdf.
Land Use Factors That Affect Travel
Walking and
Cycling Conditions
Site Design and
Building Orientation
Transportation
Demand Management
Modeling Land Use Impacts on Travel Behavior
Related Chapters and Resources
References And Resources For More Information
Introduction
Land Use (also called Land Development, Spatial Development, Community Design, Urban Design, Cityscape or The Built Environment) refers to various land use factors, such as density, mix, connectivity and the quality of the pedestrian environment, as summarized in Table 1.
Table 1 Land Use Factors
|
Factor |
Definition |
|
Density |
People or jobs per hectare. |
|
Regional Accessibility |
A site’s location relative to the regional urban center, and the number of jobs and public services available within a given travel time |
|
Centeredness |
Degree to which commercial and other public activities are located in downtowns and other activity centers. |
|
Land Use Mix |
Degree to which residential, commercial and institutional land uses are located close together. |
|
Connectivity |
Degree to which roads and paths are connected and allow direct travel between destinations. |
|
Roadway Design |
Scale and design of streets, and how various uses are managed. Traffic calming refers to street design features intended to reduce traffic speeds and volumes. |
|
Walking & Cycling Conditions |
Quality of walking and cycling transport conditions. (Active transport is a general term for walking, cycling, and their variants). |
|
Transit Accessibility |
Degree to which destinations are accessible by quality public transit. |
|
Parking Management |
Number of parking spaces per building unit or hectare. Parking management includes pricing and regulations |
|
Site Design |
|
|
Transportation Demand Management |
Various strategies and programs that encourage more efficient travel patterns, often implemented as an alternative to road and parking facility expansion, and in conjunction with land use policy reforms. |
This table describes various land use factors that can affect travel behavior and population health.
These factors affect travel behavior by affecting the distances that need to be traveled between destinations, and the relative efficiency of different modes. Some TDM strategies change land use patterns directly (Smart Growth, Access Management, Transit-Oriented Development, Location-Efficient Development, Road Space Reallocation, Parking Management, Downtowns, Roadway Connectivity), and most TDM strategies affect land use indirectly through impacts on travel behavior. This chapter examines how land use factors affect travel behavior and therefore the effectiveness of land use planning strategies to achieve TDM objectives.
Land Use Factors That Affect Travel
This section describes how different land use factors affect travel patterns.
Density and Clustering
Density refers to the number of people or jobs in a given area (Campoli and MacLean, 2002). Clustering refers to related activities located close together, often in Commercial Centers. Density and clustering can be measured at various scales: regional, county level, municipal jurisdiction, neighborhood, census tract, city block or individual campuses and buildings. Density and clustering affect travel patterns through the following mechanisms:
· Land Use Accessibility.
The number
of potential destinations located within a geographic area tends to increase
with population and employment density, reducing travel distances and the need
for automobile travel. For example, in low-density areas a school may serve
hundreds of square miles, requiring most students to travel by motor vehicle.
In higher density areas, schools may serve just a few square miles, reducing
average travel distances and allowing more students to walk or cycle.
Similarly, average travel distances for errands, commuting and
business-to-business transactions can decline with density.
· Transportation
Diversity. Increased density tends to increase the number of transportation
options available in an area due to economies of scale. Higher density areas
tend to have better sidewalks, bicycle facilities and transit service because
increased demand makes them more cost effective.
· Reduced Automobile
Accessibility.
Increased density tends to reduce traffic speeds, increase traffic congestion
and reduce parking supply, making driving relatively less attractive than
alternative modes.
As a result of these factors, increased density and
clustering tend to reduce per capita automobile ownership and use, and increase
use of alternative modes (
Figure 1 Annual
VMT Per Household (Holtzclaw, 1994)
This figure illustrates how density and transit accessibility affect household vehicle mileage. The Transit Accessibility Index (TAI) indicates daily transit service nearby.
Holtzclaw (1994) and Holtzclaw, et al (2002) find that average vehicle ownership, vehicle travel, and vehicle expenditure per household decline with increasing residential densities and proximity to public transit, holding constant other demographic factors such as household size and income. The This View of Density Calculator (www.sflcv.org/density) uses this model to predict the effects of different land use patterns on travel behavior. For example, a reduction from 20 to 5 dwelling units per acre (i.e., urban to suburban densities) increases average vehicle travel and automobile expenditures by about 40%.
Density at both origins and destinations affect travel behavior. One study found that increasing urban residential population density to 40 people per acre increased transit use from about 2% to 7%, while increasing densities in Commercial Centers to 100 employees per acre resulted in an additional 4% increase in transit use, to an 11% total mode share (Frank and Pivo, 1995). Both work trips and shopping trips are affected by population and employment densities.
Some of the differences in travel behavior between higher and lower density land use patterns may result from demographic sorting (also called self-selection). People who cannot drive are more likely to choose homes in older, higher-density urban neighborhoods, and some of these neighborhoods have low average household incomes, which also tends to reduce per capita vehicle travel. However, studies that account for demographic factors find that virtually all groups that live in higher density areas reduce their average annual vehicle mileage (Cambridge Systematics, 1994; Holtzclaw, 1994).
Regional Accessibility
Regional accessibility refers to an individual site’s location relative to the regional urban center (either a central city or a central business district [CBD]), or other major employment centers, and the portion of residents, employment and activities located close to that center (Kuzmyak and Pratt, 2003; Ewing, 1995).
Although regional accessibility tends to have little effect on total trip generation (the total number of trips people make), it tends to have a major effect on trip length and therefore per capita vehicle travel. People who live and work several miles from a city tend to drive significantly more annual miles than if located in the same type of development closer to the urban center. Kockelman (1997) found that accessibility (measured as the number of jobs within a 30-minute travel distance) was one of the strongest predictors of household vehicle travel, stronger than land use density.
Travel time maps use isochrones (lines of constant time) to indicate the time needed to travel from a particular origin to other areas (Lightfoot and Steinberg, 2006). For example, areas within one hour may be colored a dark red, within two hours a lighter red, within three hours a dark orange, and within four hours a light orange. Maps can indicate and compare travel times by different modes. For example, one set of maps could show travel times for automobile travel and another for public transit travel. Travel time maps are an indication of accessibility.
Centeredness
Centeredness refers to the portion of employment,
commercial, entertainment, and other major activities concentrated in multi-modal
Centers, such as central business districts (CBDs),
downtowns and large industrial parks. Such centers reduce the amount of travel
required between destinations and are more amenable to alternative modes,
particularly public transit. People who work in major multi-modal activity
centers tend to commute by transit significantly more than those who work in
more dispersed locations, and they tend to drive less for errands. Centeredness affects overall regional travel, not just the
trips made to the center. For example, Los Angeles is one of the densest cities
in North America, but it lacks strong centers, and so is relatively automobile
dependent, with higher rates of vehicle ownership and use than cities such as
Chicago, which have similar density but stronger centers (Ewing, Pendall
and Chen, 2002).
Land Use Mix
Land Use Mix refers to locating different types of land uses (residential, commercial, institutional, recreational, etc.) close together. This can occur at various scales, including mixing within a building (such as ground-floor retail, with offices and residential above), along a street, and within a neighborhood. It can also include mixing housing types, so an area contains a variety of demographic and income classes. Such mixing is normal in cities and is a key feature of New Urbanism.
Increased land use mix tends to reduce the distances that residents must travel for errands and allows more use of walking and cycling for such trips. It can reduce commute distances (some residents may obtain jobs in nearby businesses), and employees who work in a mixed-use commercial area are more likely to commute by alternative modes (Modarres, 1993; Kuzmyak and Pratt, 2003). Certain combinations of land use are particularly effective at reducing travel, such as incorporating schools, stores, parks and other commonly-used services within residential neighborhoods and employment centers. This creates urban villages, which are walkable centers and small neighborhoods that contain the services and activities people most often need. The table below summarizes the results of one study concerning how various land use features affected drive-alone commute rates. Important amenities include bank machines, cafes, on-site childcare, fitness facilities, and postal services.
Table 2 Drive
Alone Share At Worksites Based on Land Use Characteristics (Cambridge Systematics, 1994, Table 3.12)
|
Land Use Characteristics |
Without |
With |
Difference |
|
Mix of Land Uses |
71.7 |
70.8 |
-0.9 |
|
Accessibility to Services |
72.1 |
70.5 |
-1.6 |
|
Preponderance of Convenient Services |
72.4 |
69.6 |
-2.8 |
|
Perception of Safety |
73.2 |
70.6 |
-2.6 |
|
Aesthetic Urban Setting |
72.3 |
66.6 |
-5.7 |
Jobs/Housing Balance refers to the ratio of residents and jobs in an area. Research indicates that a jobs/housing balance of about 1.0 tends to reduce average commute distance and per capita vehicle travel (Weitz, 2003; Kuzmyak and Pratt, 2003). In some situations, suburban dispersion of employment can reduce average commute distance, although it tends to increase total per-capita vehicle travel. Crane and Chatman (2003) find that a five percent increase in the amount of employment in a metropolitan area’s outlying counties will lead to a 1.5 percent reduction in the average commute distance, with significant differences by industry. The suburbanization of construction, wholesale, and service employment is associated with shorter commutes, while manufacturing and finance deconcentration (weakly) explain longer commutes. However, this may be offset by increased non-work vehicle mileage.
Connectivity
Connectivity refers to the degree to which a road or path system is connected, and therefore the directness of travel between destinations (“Connectivity,” VTPI, 2005). A hierarchical road network with many dead-end streets that connect to a few major arterials provides less accessibility than a well-connected network. Increased connectivity reduces vehicle travel by reducing travel distances between destinations and by improving walking and cycling access, particularly where paths provide shortcuts, so walking and cycling are relatively direct.
Connectivity can be evaluated using various indices (Handy,
Roadway Design
Roadway
design can affect travel behavior in several ways. A Connected
road network provides better Accessibility than a
conventional hierarchical road network with a large portion of dead-end streets
(Handy,
A USEPA study (2004) found that regardless of population density, transportation system design features such as greater street connectivity, a more pedestrian-friendly environment, shorter route options, and more extensive transit service have a positive impact on urban transportation system performance, (per-capita vehicle travel, congestion delays, traffic accidents and pollution emissions), while roadway supply (lane-miles per capita) had no measurable effect. The Smart Growth Index (USEPA, 2002) describes a methodology for calculating the effects of increased roadway connectivity on vehicle trips and vehicle travel.
Traffic Calming, Streetscaping and Walking and Cycling Improvements can also affect travel behavior. Cervero and Kockelman (1997) find that residents of neighborhoods with connected street networks and limited commercial parking rely more on alternative modes for non-work trips and drive significantly less than residents of conventional suburban neighborhoods. Residents in a pedestrian friendly community walked, bicycled, or rode transit for 49% of work trips and 15% of their non-work trips, 18- and 11-percentage points more than residents of a comparable automobile oriented community (Cervero and Radisch, 1995). Another study found that walking is three times more common in a community with pedestrian friendly streets than in otherwise comparable communities that are less conducive to foot travel (Moudon, et al, 1996).
Parking Management
Parking Management refers to the supply, price and regulation of parking facilities. How parking is managed can significantly affect travel behavior. As parking becomes more abundant and cheaper, automobile ownership and use increase and destinations become more dispersed, reducing land use Accessibility. Parking supply and pricing have a significant impact on commute mode split (Morrall and Bolger, 1996; Shoup, 1997).
Transit-Oriented Development
Transit Oriented Development (TOD) refers to communities designed to provide convenient access to high-quality transit services. Several studies indicate that TOD can significantly reduce per capita automobile travel (Cervero, et al, 2004). This occurs because some trips shift to transit, and because transit stations often serve as a catalyst for more accessible land use, creating higher density, mixed-use, walkable Centers. People who live or work in such areas tend to own fewer cars, drive less and use transit more than in other locations (Cambridge Systematics, 1994). As a result of these various factors, Transit Oriented Development tends to “leverage” much greater reductions in vehicle travel than what is directly shifted from automobile to transit (Litman, 2005). Cervero, et al. (2004) develop a model for predicting the effects of increased residential and commercial density, and improved walkability around a station on transit ridership. For example, increasing residential density near transit stations from 10 to 20 units per gross acre increases transit commute mode split from 20.4% to 24.1%, and up to 27.6% if implemented with pedestrian improvements.
The table below shows trip reduction predictions for travel
impacts of development location and design factors used in
Table 3 Trip
Reduction of Development Location, Design and Density (
|
Minimum Floor Area
Ratio |
Mixed-Use |
Commercial
Near Bus |
Commercial
Near LRT Station |
Mixed-Use
Near Bus |
Mixed-Use
Near LRT |
|
No minimum |
- |
1% |
2.0% |
- |
- |
|
0.5 |
1.9% |
1.9% |
2.9% |
2.7% |
3.9% |
|
0.75 |
2.4% |
2.4% |
3.7% |
3.4% |
4.9% |
|
1.0 |
3.0% |
3.0% |
5.0% |
4.3% |
6.7% |
|
1.25 |
3.6% |
3.6% |
6.7% |
5.1% |
8.9% |
|
1.5 |
4.2% |
4.2% |
8.9% |
6.0% |
11.9% |
|
1.75 |
5.0% |
5.0% |
11.6% |
7.1% |
15.5% |
|
2.0 |
7.0% |