Transport Model Improvements
Improving Methods for Evaluating the Effects and Value of Transportation System Changes
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Victoria Transport Policy Institute
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Updated 21 March 2019
This chapter describes various ways to reform transport models to increase their accuracy when comparing modes and evaluating TDM strategies. Current models tend to be biased in various ways that exaggerate the benefits of roadway capacity expansion and undervalue the impacts and benefits of TDM strategies that encourage use of alternative modes and reduce motor vehicle travel.
Models are simplified descriptions of a system used to predict and evaluate the results of system changes. Various transport models are used to predict impacts and evaluate options for Transport Planning and Evaluation.
The assumptions and analysis methods used in these models can affect planning decisions. Commonly used models tend to undervalue alternative modes and TDM solutions in various ways. TDM planning requires models that can predict the impacts of various changes, such as improvements in alternative modes, pricing reforms and marketing strategies.
Several types of models are used for transport planning:
Travel demand models (also called traffic models) are designed to evaluate Transport Demands (the amount of travel people would choose under specific conditions of price, transport services and land use policies) and use this information to predict roadway traffic volumes and impacts such as congestion and pollution emissions. Most are four-step models, meaning that they follow these steps:
1. Trip generation. Predict total trips that start and end in a particular area (called Traffic Analysis Zones or TAZs), based on factors such as each zone’s land use patterns, number of residents and jobs, demographics, transportation system features (number of roads, quality of transit service, etc.), and distance between two zones.
2. Trip distribution. Trips are distributed between pairs of zones, based on the distance between those zones.
3. Mode share. Trips are allocated among the available travel modes (usually auto and transit).
4. Route assignment. Trips are assigned to specific facilities included in the highway and transit transportation networks.
These models use travel survey and census data to determine Transport Demands, establish baseline conditions and identify trends. Trips are often predicted separately by purpose (i.e., work, shopping, other) and then aggregated into total trips on the network. Because they are designed primarily to identify congestion problems they mainly measure peak-period motor vehicle trips on major roadways. The generally report roadway Level-of-Service (LOS), which is a letter grade from A (best) to F (worst) that indicates vehicle traffic speed and delay.
These models often incorporate several types of bias favoring automobile transport over other modes and undervaluing TDM strategies. The travel surveys they are based on tend to ignore or undercount Nonmotorized travel and so undervalue nonmotorized transportation improvements for achieving transportation planning objectives (Stopher and Greaves 2007). Most do not accurately account for the tendency of traffic to maintain equilibrium (congestion causes travelers to shift time, route, mode and destination) and the effects of generated traffic that results from roadway capacity expansion, and so tend to exaggerate future congestion problems and the benefits that result if roadway capacity is expanded. They are not sensitive to the impacts many types of TDM strategies have on trip generation and traffic problems, and so undervalue TDM benefits (Schneider, Handy and Shafizadeh 2014).
Publications such as the Institute of Transportation Engineers (ITE) Trip Generation and Parking Generation reports summarizes information from numerous site surveys that measure the number of vehicle trips and vehicles parked in various land use types. This information is used to predict the impacts that future developments will have on local traffic volumes and the number of parking spaces required. The resulting values influence planning decisions in various ways, including transportation impact fees and minimum parking requirements for new development.
These reports have been criticized on a number of grounds. Most of their input surveys were performed in automobile-oriented suburban locations were all parking is on-site, since those tend to be the easiest sites to measure (it is more difficult to measure the trip and parking generation of urban sites since motorists often park off-site), and because they seldom indicate geographic, demographic and management factors that affect trip and parking generation - for example, they seldom indicate the types and incomes of people who live or work at a site, transit service proximity and quality, local walkability, whether parking is free or priced, whether the site has transportation or parking management programs. As a result, the trip and parking generation values tend to be much higher than would occur in more accessible, multi-modal locations, or for sites that implement demand management programs. These models provide little guidance for evaluating the impacts and benefits of smart growth, transportation and parking management strategies. A number of recent studies have examined ways to better predict how smart growth locations and demand management programs can affect trip and parking generation (Lee, et al. 2012; TCRP 2014).
Several models have been developed to predict how transport and land use changes (more sidewalks and paths, more compact and mixed development, etc.) affect walking and cycling activity (Abley and Turner 2011; Clifton, et al. 2015; Frank, et al. 2010; Handy, Tal and Boarnet 2010; Kuzmyak, et al. 2014; Pratt, et al 2012; Sciara, Handy and Boarnet 2016), although most are limited in the range of factors they consider and the types of travel they predict.
Economic models are used to evaluate and compare the value of particular transportation improvements, such as the benefits of widening a roadway, improvement public transit service or implementing a TDM strategy. They compare various categories of benefits and costs. They tend to consider a relatively limited set of benefits, since most of these models were originally developed to evaluate roadway improvement options and generally assume that total vehicle mileage is constant, and so are not well designed to evaluate the full benefits of TDM strategies that reduce automobile trips. For example, these models often ignore parking and vehicle ownership cost savings that result when travelers shift from automobile travel to alternative modes, and they generally ignore the safety benefits that result from reductions in total vehicle mileage (Ellis, Glover and Norboge 2012).
These models are designed to predict how transportation improvements will affect land use patterns, for example, the location and type of development that will occur if a highway or transit service is improved. They are often integrated with traffic models. These are considered the best tools for evaluating transportation policies and programs because they can measure Accessibility rather than just mobility, but are costly to develop and complex, and so may be difficult to apply, particularly for evaluating individual, small-scale projects. Some models predict how particular land use factors, such as density and mix, affect travel behavior, and their impacts on congestion and pollution emissions (Bartholomew and Ewing 2009; Mehaffy 2015; Scheurer, Horan and Bajwa 2009). The Smart Growth Area Planning (SmartGAP) tool synthesizes households and firms in a region and determines the travel demand characteristics of these households and firms based on the characteristics of their built environment and transportation policies affecting their travel behavior (TRB 2012).
Some models evaluate accessibility based on the number of services and activities (such as jobs) that can be reached within a given time period by various travel modes (Levine, et al 2012; Levinson 2013). Simplified versions include WalkScore and TransitScore, and Google Maps commute travel time applications.
This newer approach models the behavior and needs of individual transport users (called agents), rather than aggregate groups, which improves consideration of modes such as walking and cycling, the Transport Demands of non-drivers, cyclists and the disabled, and the effects of factors such as parking supply and price, transit service quality, and local land use accessibility factors. Simulation models can provide a bridge between other types of models, since they can incorporate elements from the conventional traffic, economic and land use models. Simulation models have been used for many years on individual projects, and are increasingly used for area-wide analysis.
Various models are used to predict how specific transport policies and projects will affect vehicle fuel consumption and pollution emissions (“Effects of Travel Reduction,” USDOE 2013). Conventional models often exaggerate roadway expansion emission reductions and undervalue TDM strategies (Litman 2013).
Some special models have been developed to help evaluate particular types of TDM programs, such as the Commuter Model (USEPA 2005), the TRIMMS model (USF 2006) and which can predict the effects of Commute Trip Reduction programs on commute travel behavior.
Price elasticities are defined as the percentage change in consumption of a good caused by a one-percent change in its price or other characteristics (such as traffic speed or road capacity). For example, an elasticity of -0.5 for vehicle use with respect to vehicle operating expenses means that each 1% increase in these expenses results in a 0.5% reduction in vehicle mileage or trips. Economists have collected information on transportation price elasticities, including how changes in transit service quality and fares affect transit ridership (Litman 2004; DfT 2010), the effects of changes in parking fees, fuel price and road tolls on vehicle travel (Litman 2006), and information on how various Land Use Factors Affect Travel Behavior.
Conventional transport models can incorporate a number of possible omissions and biases (Bruun 2014, Chapter 7). Because conventional models primarily measure motor vehicle travel rather than Accessibility they tend to undervalue alternative modes and alternative ways of improving accessibility, such as Smart Growth land use. For example, if a planning decision involves trade-offs between automobile access and pedestrian or transit access, conventional models recognize the impacts on automobile access (such as changes in traffic speeds) but ignores impacts on other modes (such a barriers to pedestrian travel or greater distances between transit stations and local destinations). The results are both inefficient (they undervalue what may be the most cost effective way to improve transportation) and Inequitable, since they tend to overlook travel activities and needs of non-drivers.
Travel models tend to focus on quantitative factors (travel speed, operating costs and crash rates) and undervalue qualitative factors such as travel convenience, comfort and security (Litman 2007a). Conventional traffic models often use simplified travel time cost functions which assumes that any shift from driving to an alternative mode increases travel time costs. This is wrong for two reasons. First, alternative modes are sometimes as fast as driving. Cycling is often as fast as driving for short trips, door-to-door. Ridesharing and transit are sometimes faster than driving with grade separated systems or HOV Priority. Second, travelers sometimes prefer using alternative modes even if they are slower than driving, because they are less stressful or enjoyable (particularly walking and cycling). This tends to favor higher speed modes, such as automobile travel, and undervalues improvements to alternative modes.
Most large Metropolitan Planning Organizations (MPOs) run their travel models with Full Feedback, meaning all model steps are run until the model equilibrated (results in each step no longer change with more iterations). This allows models to indicate how congestion affects trip lengths. This is an important and simple model improvement that helps predict the induced travel effects of adding or expanding roadways. This satisfies the National Environmental Policy Act (NEPA) requirements. This is demonstrated by showing very little change in some output, between model runs N and N+1, called the Convergence Criterion. Some official Federal software, including STEAM (which must be used for new rail starts analyses) have induced travel factoring in them. The U.S. Clean Air Act Air Quality Conformity Rule requires this in regions with Severe and worse air quality ratings. Most model shells (software packages) can now do this, and this type of modeling is common practice in good MPOs, but many MPOs perform too few model iterations to achieve full equilibrium.
To show effects on trip generation (number of trips per day per household) models require an Auto Ownership step at the front end, that is, the model must be upgraded from four to five steps. Many MPOs have done this. Reduced automobile congestion tends to induce slightly higher vehicle ownership rates which slightly increases Trip Generation. This effect is generally small.
The third model improvement is to put land use variables in the Mode Choice step, and increase land use density and mix, which results in more walk and bike trips and fewer car trips. Some large MPOs have done this, but few middle-sized and small ones. This is relatively easy and inexpensive to incorporate. Portland Metro did all of these in 1991 and the Sacramento MPO (SACOG), did them in 1994.
Another major area of model improvement is adding a land use model to evaluate how land use factors affect travel behavior and how transportation planning decisions affect land use development patterns. About half of the induced travel effect is actually caused by more sprawl, in situations where sprawl land use scenarios are being studied. The simplest way to model these impacts is to use an Expert Panel. The panel marks up maps indicating where growth is likely to increase if a transportation facility is improved. The Conformity rule sort of requires this for regions with Severe and worse air quality ratings. However, panel members can be handpicked to be biased, for example, to understate the amount of land use development that is likely to be induced by a highway improvement, which will understate induced travel effects. Simple GIS-based land use models are available, such as UPlan, which is available free and can be set up and run in days if a MPO has a GIS section and appropriate data (Johnston 2004).
The biases in current models tend to exaggerate the benefits of roadway capacity expansion and understate the value of alternative modes and TDM solutions (Ewing, et al. 2007; Kuzmyak 2012). More accurate and Comprehensive modeling is therefore a key step in developing more optimal Transport Planning and implementing specific TDM strategies such as Lease Cost Planning, Transit Improvements and Smart Growth land use policies. The table below describes various problems common with current models and how they could be corrected. These deficiencies are not necessarily intrinsic, significant improvements can be made to existing models and how they are applied. For example, many problems could be reduced by simply educating planners and decisions-makers about modeling assumptions, biases and weaknesses, so they can take these factors into account.
Current models can be improved in various ways summarized in Table 1.
Table 1 Improving Transport Models
|
Factor |
Problems With Current Models |
Appropriate Corrections |
|
Accessibility |
Most transportation models primarily evaluate mobility (movement), and fail to reflect accessibility (people’s ability to obtain desired goods and activities). |
Develop multi-modal models which indicate the quality of nonmotorized and transit travel, and integrated transportation/land use models which indicate accessibility. |
|
Modes considered |
Most current models only consider automobile and public transit. |
Expand models to evaluate other modes, including walking and cycling. |
|
Travel data |
Travel surveys often undercount short trips, non-motorized travel, off-peak travel, etc. |
Improve travel surveys to provide more comprehensive information on travel activity. |
|
Consumer Impacts |
Most economic evaluation models apply relatively crude analysis of consumer impacts. For example, they assume that shifts from driving to slower modes increase costs. |
Use consumer surplus analysis to measure the value to users of transport system changes. Recognize that shift to slower modes in response to positive incentives provide overall benefits. |
|
Travel time
|
Most models apply the same travel time value to all travel, regardless of conditions. |
Vary travel time cost values to reflect travel conditions, such as discomfort and delay. |
|
Generated traffic and induced travel |
Traffic models fail to account for the tendency of roadway expansion to generate additional peak-period traffic, and the additional costs from induced travel. |
Incorporate various types of feedback into the traffic model. Develop more comprehensive economic analysis models which account for the economic impacts of induced travel. |
|
Qualitative impacts |
Focus on quantitative factors such as speed and user fees, and undervalues qualitative factors such as convenience and comfort. Level-of-service ratings are provided for roadway conditions but not other modes. |
Develop Multi-Modal Level-of-Service rating systems to help evaluate walking, cycling and public transit travel conditions, in order to identify problems and trade-offs between automobile traffic and other modes. |
|
Nonmotorized travel |
Most travel models do not accurately account for nonmotorized travel and so undervalue nonmotorized improvements. |
Modify existing models or develop special models for evaluating nonmotorized transportation improvements. |
|
Impacts Considered |
Current models only measure a few impacts (travel time and vehicle operating costs). |
Use more comprehensive impact analysis, including crash risk, pollution emissions, pedestrian delays and land use impacts, etc. |
|
Transit elasticities |
Transit elasticity values are largely based on short- and medium-run studies, and so understate long-term impacts. |
Use more appropriate values for evaluating long-term impacts of transit fares and service quality. |
|
Self-fulfilling prophesies |
Modeled traffic projections are often reported as if they are unavoidable. This creates self-fulfilling prophecies of increased roadway capacity, generated traffic, increased traffic problems and sprawl. |
Report travel demand as a variable (“traffic will grow 20% if current policies continue, 10% if parking fees average $1 per day, and 0% if parking fees average $3 per day.”) rather than a fixed value (“traffic will grow 20%”). |
|
Construction impacts |
Economic models often fail to account for construction activity external costs such as congestion and pollution. |
Take congestion delays into account when evaluating projects and comparing capacity expansion with TDM solutions. |
|
Transportation diversity |
Models often underestimate the benefits of improved travel options, particularly those used by for disadvantaged people. |
Recognize the various benefits that result from improving accessibility options. |
|
Impacts on land use |
Models often fail to identify how transport decisions will affect land use patterns, how this affect accessibility and strategic planning objectives. |
Develop integrated transportation and land use planning models which predict how transport decisions affect land use patterns and how land use decisions affect accessibility. |
This table summarizes common problems with current transportation models, and ways to correct those problems. These improvements are particularly important for evaluating alternative modes and mobility management strategies.
Multi-modal accessibility models measure the travel time, and sometimes also the financial costs, required to reach destinations by various modes. Below are examples of such models.
This is a leading resource for the research and application of accessibility-based transportation system evaluation. It sponsors the Access to Destinations (http://access.umn.edu) interdisciplinary research program by the University of Minnesota’s Center for Transportation Studies which is developing tools and data sets to quantify overall accessibility, taking into account multiple modes (walking, cycling, public transit and automobile) and land use development patterns. .
The Access to Jobs interactive mapping system shows the number of suitable jobs available within a given commute travel time by various travel modes and job categories. It was produced as part of the Fragile Success (http://fragile-success.rpa.org) regional performance evaluation which examines economic, social and environmental tends in the New York metropolitan region for strategic planning purposes (RPA 2014). The study, Mobility, Economic Opportunity and New York City Neighborhoods (Kaufman, et al. 2014), provides neighborhood-scale information on job access.
Access Scores uses GIS mapping tools to develop Access Scores which measure accessibility to work and common non-work activities, and how a transportation system change will affect that accessibility.
This program is developing accessibility tools that help understand relationships between land use and mobility. Such a framework has important potential advantages when transferred to the realm of urban planning. Despite the large number of instruments available in literature, they are not widely used to support urban planning practices. This approach can provide significant benefits. The 2014 report, Assessing Usability of Accessibility Instruments (www.accessibilityplanning.eu/reports/report-2) summarizes key findings.
Shah and Adhvaryu (2016) developed a GIS mapping tool in Ahmedabad, India that generates a visual representation of public transport accessibility levels (PTAL) taking into account average walk speed and time, distances to public transport stops, and peak-hour route frequencies of different public transport modes. This demonstrates that such tools can function in developing as well as developed countries. This tool can be used to help planning, such as formulating development/master plans with land use–transport integration, prioritizing public transport and supporting investments, formulating parking policies, and developing transit-oriented zoning regulations.
The Brookings Institution’s Moving to Access (MTA) Initiative aims to inform and promote a more socially focused, access-first approach to urban transportation policy, planning, investment, and services. Cities and metropolitan areas globally are looking to adopt new, actionable metrics to guide more purposeful initiatives to improve accessibility for people of all incomes. The MTA Initiative looks to move beyond theory and accelerate the adoption of these innovative efforts, exploring new tools, techniques, and performance measures across the developing and developed world.
This program ranks locations in 350 U.S. cities based on the number of jobs that can be accessed within a 30-minute walk or transit ride.
This regional mapping, analysis and visualization program integrates a range of public and private data and performance indicators for sustainable community evaluation.
This program provides interactive maps and data for measuring location efficiency, including the effects of the built environment on per capita vehicle travel, and methods for measuring access to jobs and workers by public transportation.
CityLab’s Sugar Access is an integrated Geographic Information Systems (GIS) software program that communities can use to quantify the access (time and financial costs) of accessing various types of services and activities (healthcare, shops, schools, jobs, parks, etc.) by various travel modes in a particular area.
This US Federal Highway Administration website describes analytical methods for evaluating regional economic, social and environmental impacts of various transportation and land use policies.
This interactive mapping system shows both travel times to the city center and housing costs for various locations in London.
The Metropolitan Chicago Accessibility Explorer is an easy-to-use mapping system that measures the number of activities, including various types of jobs, schools, parks, stores and libraries, that can be reached by residents of a specified neighborhood within a given amount of travel time, by a particular mode and time of day in the Chicago Metropolitan area. The results are displayed on maps which can be adjusted by scale and area. The Accessibility Explorer was developed by the Department of Urban Planning and Policy at University of Illinois at Chicago help policy makers, planners and the general public easily evaluate how transportation system and land use change could alter accessibility.
The City of Portland (2012) uses GIS mapping to evaluate the number of commonly-used services that can be accessed within a 20-minute walk of residences, taking into account sidewalk conditions, natural and roadway barriers, street connectivity and topography.
Transport Model Improvements are generally implemented by local or regional transportation agencies, often with the support of higher levels of government, professional organizations and academic institutions. Professional standards for transportation models has improved over time, so improvements in a particular community may simply involve bringing local models up to best current practices. New, more comprehensive models are being developed, including generic simulation and integrated land use models, suitable for application in more situations.
Because models affect many transportation planning decisions, Transport Model Improvements can have many travel impacts.
Table 2 Travel Impact Summary
|
Objective |
Rating |
Comments |
|
Reduces total traffic. |
2 |
|
|
Reduces peak period traffic. |
2 |
|
|
Shifts peak to off-peak periods. |
2 |
|
|
Shifts automobile travel to alternative modes. |
2 |
|
|
Improves access, reduces the need for travel. |
2 |
|
|
Increased ridesharing. |
2 |
|
|
Increased public transit. |
3 |
|
|
Increased cycling. |
3 |
|
|
Increased walking. |
3 |
|
|
Increased Telework. |
2 |
|
|
Reduced freight traffic. |
2 |
|
Rating from 3 (very beneficial) to –3 (very harmful). A 0 indicates no impact or mixed impacts.
Transport Model Improvements lead to more cost effective planning, particularly implementation of TDM strategies and more accessible land use.
Table 3 Benefit Summary
|
Objective |
Rating |
Comments |
|
Congestion Reduction |
2 |
Supports development of more efficient transport system. |
|
Road & Parking Savings |
2 |
" |
|
Consumer Savings |
2 |
" |
|
Transport Choice |
2 |
" |
|
Road Safety |
2 |
" |
|
Environmental Protection |
2 |
" |
|
Efficient Land Use |
3 |
" |
|
Community Livability |
2 |
" |
Rating from 3 (very beneficial) to –3 (very harmful). A 0 indicates no impact or mixed impacts.
Transport Model Improvements tend to better identify the full impacts of transportation decisions, including external impacts such as traffic congestion, parking costs, accident risks and pollution emissions, and so can help reduce these impacts. It also tends to support development of more balanced and efficient transportation systems, which tends to benefit disadvantaged people and improve basic mobility. Multi-Modal Level-of-Service analysis is particularly helpful for identifying problems facing non-drivers and trade-offs between automobile traffic and other modes.
Table 4 Equity Summary
|
Criteria |
Rating |
Comments |
|
Treats everybody equally. |
1 |
|
|
Individuals bear the costs they impose. |
3 |
Better identifies external costs of planning decisions. |
|
Progressive with respect to income. |
2 |
Supports development of more balanced transport system. |
|
Benefits transportation disadvantaged. |
2 |
" |
|
Improves basic mobility. |
2 |
" |
Rating from 3 (very beneficial) to –3 (very harmful). A 0 indicates no impact or mixed impacts.
Transport Model Improvements can be applied in many situations, particularly rapidly-growing urban areas. It is usually implemented by state, regional and local governments.
Table 5 Application Summary
|
Geographic |
Rating |
Organization |
Rating |
|
Large urban region. |
3 |
Federal government. |
2 |
|
High-density, urban. |
3 |
State/provincial government. |
3 |
|
Medium-density, urban/suburban. |
3 |
Regional government. |
3 |
|
Town. |
2 |
Municipal/local government. |
3 |
|
Low-density, rural. |
1 |
Business Associations/TMA. |
1 |
|
Commercial center. |
2 |
Individual business. |
1 |
|
Residential neighborhood. |
2 |
Developer. |
1 |
|
Resort/recreation area. |
2 |
Neighborhood association. |
1 |
|
College/university communities. |
2 |
Campus. |
1 |
Ratings range from 0 (not appropriate) to 3 (very appropriate).
Policy Reform
Transport Model Improvements support most other TDM strategies. It is closely related to Institutional Reforms, Least-Cost Planning, Comprehensive Transportation Planning, Prioritizing Transportation, Traffic Operations, Multi-Modal Level-of-Service, Change Management, and Contingency-Based Planning.
Transport engineers and planning are the key stakeholders for implementing Transport Model Improvements. Transportation agencies may need to change their practices. The public may become more directly involved in transportation decision-making.
There are a variety of technical and institutional barriers to improving transport models. Some improvements require more data or more advanced computer systems, which may have significant costs, although these costs can be minimized by identifying required changes early in the planning process, for example, before travel surveys are performed or new computer equipment purchased.
There are several good sources of information on best current practices for transportation modeling (Pike 2011) and ways to improve the accuracy of common models (Tian, et al. 2015). The U.S. DOT’s Travel Model Improvement Program and the U.S. Federal Highway Administration’s Toolbox for Regional Policy Analysis Website provide current information on transportation modeling techniques. The Comprehensive Transport Planning chapter of this Encyclopedia and Litman (2004b) describes more accurate and comprehensive transportation economic evaluation methods. Below are some general guidelines for transportation modeling best practices:
· Use best current practices for traffic and transportation models. Modelers should work to stay abreast of current research and improvements.
· Use comprehensive travel surveys to track travel activity, including nonmotorized travel, short trips, travel by children, and off-peak travel.
· Improve modeling and evaluation of Nonmotorized and Transit travel.
· Develop Multi-Modal Level-of-Service indicators to help evaluate nonmotorized and public transit conditions and service quality, and potential conflicts between automobile transportation and other modes.
· Incorporate feedback into models in order to accurately predict future congestion problems and the traffic generated by roadway capacity expansion.
· Develop integrated transportation and land use models that can predict the effects that transportation decisions will have on land use development patterns, and the effects that land use decisions will have on accessibility.
· Use comprehensive economic evaluation models which account for all significant impacts, including road and parking facility costs, consumer costs, accidents, pollution emissions, and impacts on land use development patterns. This model should give particular consideration to external costs and impacts on people who are transportation disadvantaged.
Table 6 summarizes various model that can be used to evaluate how land use factors affect travel behavior, energy consumption and pollution emissions.
Table 6 Models for Evaluating Land Use Impacts on Travel Activity (Vernez Moudon and Stewart 2013)
|
Tool |
Developer |
Description |
URL |
Applications |
|
Spreadsheet Tools |
||||
|
CCAP Transportation Emissions Guidebook Emissions Calculator |
Center for Clean Air Policy |
Estimates GHG and other emissions based on TDM policies and Vehicle technologies |
Unknown |
|
|
COMMUTER |
US EPA |
Estimates travel and emissions impacts of commuting programs |
Unknown |
|
|
Conserve by Bicycling and Walking |
FDOT |
Estimates corridor-level NMT and co-benefits from area BE and demographic factors |
http://www.dot.state.fl.us/safety/4-Reports/Bike-Ped-Reports.shtm |
Florida |
|
King County State Environmental Policy Act (SEPA) GHG Emissions Worksheet |
King County, Washington |
Estimates all GHG emissions from a development project (has not been updated since 2007) |
http://your.kingcounty.gov/ddes/forms/SEPA-GHGEmissionsWorksheet-Bulletin26.xls |
King County, WA |
|
Rapid Fire |
Calthorpe Associates |
Models VMT, GHG emissions, etc. based on land use scenarios |
California, Honolulu |
|
|
VMT reduction: Phase One |
WSDOT |
Estimates neighborhood residential VMT and CO2 based on BE and demographic factors |
Rainier Beach and Bitter lake, Seattle |
|
|
VMT Spreadsheet |
Fehr and Peers |
Estimates mobile GHG emissions from land use development projects. |
Northgate, Seattle |
|
|
VMT Spreadsheet with Smart Growth Adjustments |
Fehr and Peers |
Estimates mobile GHG emissions from development adjusted for BE characteristics. |
Northgate, Seattle |
|
|
GIS and/or model-based tools |
||||
|
Bay Area Simplified Simulation of Travel, Energy and Greenhouse Gases (BASSTEGG) |
Bay Area Metropolitan Transportation Commission |
GIS simulation of Regional VO, VMT, and GHG based on TAZ-level BE and SES |
Bay Area, CA |
|
|
Clean Air and Climate Protection (CACP) 2009 Software |
The International Council for Local Environmental Initiatives (ICLEI) |
Estimates GHG emissions for communities based on wide range of local activity data |
Fort Collins, CO; Missoula, MT; San Diego, CA |
|
|
CommunityViz |
Placeways LLC |
GIS tool to visualize and quantify various aspects of planning |
http://placeways.com/communityviz/ |
Boston, MA;Victor, ID |
|
Energy and Emissions Reduction Policy Analysis Tool (EERPAT) |
The Federal Highway Administration (FHWA) |
State-level screening tool for GHG reduction policies on transport |
www.planning.dot.gov/FHWA_tool/ |
Florida |
|
Envision Tomorrow |
Fregonese Associates |
GIS tool that tests financial feasibility of development regulations and their impact on indicators |
www.frego.com/services/envision-tomorrow/ |
Various, including Mountlake Terrace, WA |
|
GreenSTEP |
Oregon Department of Transportation (ODOT) |
Adds GHG emissions to statewide or metro travel models that account for BE |
www.oregon.gov/ODOT/TD/TP/Pages/GreenSTEP.aspx |
Oregon |
|
Improved Data and Tools for Integrated Land Use-Transportation Planning in California |
UC Davis |
Uses California-specific relationships of BE and travel for scenario planning at multiple scales using various tools |
http://ultrans.its.ucdavis.edu/projects/improved-data-and-toolsintegrated-land-usetransportation-planning-california |
Various locations in California |
|
INDEX/SPARC |
Criterion Planners |
Map-based tool for ranking scenarios based on various performance indicators |
www.crit.com/the_tool.html |
200+ organizations in 35 states, including PSRC |
|
I-PLACE3S/PLACE3S |
California Energy Commission and the Sacramento Area Council of Governments (SACOG) |
Parcel-level GIS tool for estimating land use and transportation GHG emissions accounting for BE factors |
www.sacog.org/services/scenario-planning/ |
Sacramento area, California |
|
Local Sustainability Planning |
Southern California Association of Govts (SCAG) |
GIS tool to model land use scenarios on VO, VMT, mode share, and GHG emissions. |
http://rtpscs.scag.ca.gov/Pages/Local-Sustainability-Planning-Tool.aspx |
Various communities in Southern California |
|
Low-carb Land |
Sonoma Technology, Inc. |
Web tool for examining VMT and GHG under various growth and land use scenarios |
www.sonomatech.com/project.cfm?uprojectid=672 |
Thurston County, WA; Marin County, CA |
|
UPlan |
UC Davis Information Center for the Environment (ICE) |
Rule-based urban growth model that assigns land uses to parcels based on location attractiveness and plan requirements, for use at county or regional scale |
Shasta county, CA; Delaware Valley Transportation Commission |
|
|
Urban Footprint |
Calthorpe Associates |
GIS scenario creation and modeling tool with full co-benefits analysis capacity |
www.calthorpe.com/scenario_modeling_tools |
California, Honolulu |
|
Urbemis |
Rimpo and Associates, Inc. |
Estimates GHG emissions for development projects accounting for some BE |
www.urbemis.com |
California |
Various tools can be used to predict how specific land use development factors affect travel activity and associated pollution emissions.
A multiyear project at the University of California-Davis has compared the standard regional travel demand model, SACMET96, with two transportation-land use models, MEPLAN and TRANUS. The project evaluated a range of transportation policies and programs, including high-occupancy vehicle and high-occupancy toll lanes, light rail transit and other advanced transit, transit-oriented development, roadway capacity expansion, and road pricing. Impacts were measured for the years 2005 and 2015 on travel, emissions, user benefits, and the spatial distribution of population and employment. The results of the modeling are not always intuitive. Some of the major findings for the Sacramento region include:
The Metropolitan Washington Council of Governments commissioned the Transportation Research Board’s Committee for Review of Travel Demand Modeling to evaluate the current regional travel model and identify ways it could be improved. The Committee makes a number of recommendations for specific types of improvements, including changes in data collection and analysis to better predict travel and vehicle emissions.
Schneider, Handy and Shafizadeh (2014) find that residents of Smart Growth communities own fewer vehicles and generate about half as many trips per capita as standard models predict, and recommend adjustment factors for predicting vehicle trips in compact, multi-modal areas. Similarly, Ewing, et al. (2011) and Tian, et al. (2015) find that mixed-use development generate fewer vehicle trips that standard models predict and recommend appropriate model adjustment methods. Millard-Ball (2015) points out that many “new” trips predicted by traffic models are actually trips that would occur elsewhere if a new development is not constructed, and so recommends new methods for calculating infill development trip generation.
A few travel surveys and demand studies have been performed in developing countries and include impoverished residents. Below are examples. These can be used as models for travel surveys and demand modeling in other lower-income areas.
Judy Baker, Rakhi Basu, Maureen Cropper, Somik Lall and Akie Takeuchi (2005), Urban Poverty and Transport: The Case of Mumbai Policy, Research Working Paper 3693, World Bank (www.worldbank.org); at http://ideas.repec.org/p/wbk/wbrwps/3693.html.
Eric J. Gonzales, Celeste Chavis, Yuwei Li and Carlos F. Daganzo (2009), Multimodal Transport Modeling for Nairobi, Kenya: Insights and Recommendations with an Evidence-Based Model, Working Paper UCB-ITS-VWP-2009-5, UC Berkeley Center for Future Urban Transport; at www.its.berkeley.edu/publications/UCB/2009/VWP/UCB-ITS-VWP-2009-5.pdf.
Jacob Koch, Luis Antonio Lindau, and Carlos David Nassi (2013), Transportation in the Favelas of Rio de Janeiro, Lincoln Institute (www.lincolninst.edu); at www.lincolninst.edu/pubs/2231_Transportation-in-the-Favelas-of-Rio-de-Janeiro.
E. Kwakye, P. Fouracre and D. Ofosu-Dorte (1997), “Developing Strategies To Meet The Transport Needs Of The Urban Poor In Ghana.” World Transport Policy and Practice, Vol. 3, No. 1, pp. 8-14; at www.transport-links.org/transport_links/filearea/publications/1_639_PA3330_1998.pdf.
Debora Salon and Sumila Gulyani (2010), “Mobility, Poverty and Gender: Travel Choices of Slum Residents in Nairobi, Kenya,” Transport Reviews, Vol. 30, No. 5, pp. 641-657; summary at www.tandfonline.com/doi/abs/10.1080/01441640903298998; earlier version at www.its.ucdavis.edu/people/faculty/salon/Salon-Gulyani_Slum_2008.pdf.
Zhong Shuiying, Wei Han, Hou Weili and Cheng Dening (2003), A Lifetime of Walking: Poverty and Transportation in Wuhan, China, Economic Research Institute, Wuhan University; at www.gtkp.com/assets/uploads/20091127-162217-5468-Wuhan.pdf.
Sumeeta Srinivasan (2011), “Linking Travel Behavior and Location in Chengdu, China: Geographically Weighted Approach,” Transportation Research Record 2193, Transportation Research Board (www.trb.org), pp. 85-95; summary at http://trb.metapress.com/content/1786181773g62543.
Sumeeta Srinivasan and Peter Rogers (2005). “Travel Behavior Of Low-Income Residents: Studying Two Contrasting Locations In The City Of Chennai, India,” Journal of Transport Geography, Vol. 13, pp. 265-274; summary at www.sciencedirect.com/science/article/pii/S0966692304000535.
C. Venter, V. Vokolkova and J. Michalek (2007), “Gender, Residential Location, And Household Travel: Empirical Findings From Low-Income Urban Settlements In Durban, South Africa,” Transport Reviews, Vol. 27, No. 6, pp. 653-677; summary at http://144.171.11.39/view.aspx?id=843271.
The Access to Jobs interactive mapping system shows the number of suitable jobs available within a given commute travel time by various travel modes and job categories (RPA 2014). It was produced as part of the Fragile Success (http://fragile-success.rpa.org) regional performance evaluation which examines economic, social and environmental tends in the New York metropolitan region for strategic planning purposes.
A sketch-planning method described by Zhou and Smith (2012) can be used to project vehicle miles traveled and greenhouse gas emission reductions resulting from bicycle and pedestrian improvements. It uses standard modeling data and methods, adjusted to reflect the characteristics of non-motorized modes, including trip length, roadway conditions and terrain. This method is applied to the Going to the River project in Portland, Oregon, USA, which will provide sidewalk infill, multiuse paths, and neighborhood greenway (bicycle boulevard) improvements connecting the existing bicycle and pedestrian network to a large employment area on Swan Island.
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.
Krizek, et al. (2007) developed methods for calculating travel times by walking, cycling and public transit modes. The researchers used information on networks and speeds to construct a series of maps that graphically depict various non-auto travel networks at different points in time between 1995 and 2005. The maps break down origins and destinations into several zones (similar to watersheds). This technique makes it possible to see changes in travel time between different “travel-sheds” over time.
The Florida Department of Transportation has developed Multi-Modal Level-of-Service (LOS) rating systems which evaluate the quality of walking, cycling and public transit travel, taking into account various factors related to convenience, speed and safety. These rating can be used to compare modes, evaluate current conditions, identify problem areas and prioritize potential improvements.
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 applied 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.
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.
Project PROSPECTS is designed to help city authorities identify specific policies for more sustainable mobility, including solutions that reduce congestion, pollutants, excessive fuel consumption and carbon dioxide emissions, road accidents, and reduced accessibility for non-drivers. It should help in improving the efficiency and accessibility of the transport system, hence reducing costs and increasing competitiveness. Previous research in the OPTIMA and FATIMA projects identified strategies which increased economic efficiency by 20-30% over previously preferred strategies, using transport policy measures alone. We expect to be able to improve further on this by including land use measures. The advice will also help to identify the key barriers to implementation, and the case for overcoming them, thus facilitating the achievement of optimal strategies. In all of these ways cities competitiveness, both economically and as places to live, should be significantly enhanced.
City authorities have available an increasing range of policy measures to tackle these problems and are actively seeking integrated solutions, but it is often difficult to identify that combination of measures which will achieve the optimal strategy for a particular city. PROSPECTS is intended to provide cities with the guidance which they need in order to generate optimal land use and transport strategies to meet the challenge of sustainability in their particular circumstances.
This project includes the development and evaluation of forecasting and analysis models. It starts with a review of the requirements arising from the review of decision making requirements, and the ability of existing models to meet those requirements. The models are used both to illustrate decision making methods and to test policy options.
The principal outputs include a Decision-Makers Guidebook, designed for politicians, senior officials and the public, a Methodological Guidebook, designed for professionals, and a Policy Guidebook, which describes current experience with the full range of policy options, and is of interest to politicians, professionals and the public. These three guidebooks, covering decision making, methodology and policy advice, will be designed for ease of use by city authorities, and by the public in their cities. The advice will enable them to enhance sustainability, the environment, social inclusion and quality of life through the design of more effective land use and transport strategies.
Most transport project economic evaluation models (such as MicroBenCost and HDM-4) are designed primarily to evaluate highway improvements and so fail to account for many of the impacts that result from mode shifts and changes in total travel activity. The study, Benefit/Cost Analysis Of Converting A Lane For Bus Rapid Transit describes various benefits and costs that should be considered when evaluating the economic value of “take-a-lane” BRT systems. These include:
Benefits
Direct Benefits
Indirect Benefits
Costs
Direct Costs
Social Costs
The Greater Wasatch Region of northern Utah is a 10-county area containing three urban areas, including the greater Salt Lake City metropolitan area. The region is experiencing high growth, which has created strains on transportation infrastructure, water supply, and the natural environment. In response a public-private partnership known as Envision Utah was initiated in 1996 to study the effects of long-term growth in the region and to propose strategies to address growth-related issues. After three years of analysis and public discussion, the Envision Utah process resulted in a set of recommended actions to achieve an overall “Quality Growth Strategy.” The Envision Utah analysis showed that in 2020, compared to the baseline, the Quality Growth Strategy will conserve 171 square miles of land; include a more market-driven mix of housing (by modifying some restrictive zoning regulations); result in a 7.3% reduction in mobile source emissions; include less traffic congestion; and require $4.5 billion less investment in transportation, water, sewer, and utility infrastructure. The technical analysis was conducted through a collaborative effort among state, regional, and local agencies. These included:
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.
In 2007 the Montgomery County Council adopted a unique area-wide transportation test, called Policy Area Mobility Review (PAMR), as a growth management tool. PAMR supplements the Local Area Transportation Review process (a fairly standard transportation impact analysis of nearby intersections). PAMR signals a shift in Montgomery County from measuring traffic capacity to assessing mobility. It has two components:
· Relative Arterial Mobility (RAM), the ratio between forecasted congested travel times and free-flow travel times.
· Relative Transit Mobility (RTM), the relative speed by which journey-to-work trips can be made via transit travel as compared to auto travel
PAMR uses the regional metropolitan planning organization travel demand model to forecast conditions for a horizon year that includes previously approved development (the “pipeline”) countywide and regional growth and transportation projects funded in the next four fiscal years. The RTM and transit level of service (LOS) is established for each zone (called policy areas). The area’s arterial LOS requirements are based on the forecasted transit LOS and the RAM. For areas with adequate RAM, applicants need take no action under PAMR. For policy areas where relative arterial mobility is insufficient applicants must support the following mitigation actions:
1. Participate in a trip reduction program.
2. Provide off-site non-automobile facilities such as sidewalks or bike racks.
3. Provide and operate transit services.
4. Construct off-site roadway segments.
Researcher Karel Martens argues that current transport evaluation practices exaggerate the benefits of automobile-oriented improvements and undervalue improvements to alternative modes, which tends to be regressive because it skews planning and investment decisions to favor people who are economically, socially and physically advantaged (those who currently drive high mileage) and at the expense of those who are disadvantaged (who currently drive low mileage and rely on alternative modes). As he explains:
“Both transport modeling and cost-benefit analysis are driven by distributive principles that serve the highly mobile groups, most notably car users, at the expense of the weaker groups in society. Transport modeling is implicitly based on the distributive principle of demand. By basing forecasts of future travel demand on current travel patterns, transport models are reproducing the current imbalances in transport provision between population groups. The result is that transport models tend to generate suggestions for transport improvements that benefit highly mobile population groups at the expense of the mobility-poor. Given the importance of mobility and accessibility in contemporary society for all population groups, the paper suggests to base transport modeling on the distributive principle of need rather than demand. This would turn transport modeling into a tool to secure a minimal level of transport service for all population groups.” (Martens, 2006).
To correct these biases he recommends the following changes to transportation modeling and economic evaluation techniques to reflect equity objectives:
· Evaluate transport improvements primarily in terms of accessibility rather than mobility. For example, improvements should be rated based on the number of public services and jobs accessible to people, taking into account their ability (i.e., ability to walk and drive), travel time and financial budgets, not simply travel time savings to vehicle travelers. This recognizes the value of non-automobile modes (walking, cycling, public transit and telecommuting) and land use improvements (such as more compact and transit-oriented development) to improve accessibility and achieve transport planning objectives.
· The monetary value assigned to accessibility gains should be inversely related to people’s current levels of accessibility to reflect the principle of diminishing marginal benefits. In other words, accessibility gains for the mobility-poor (who travel lower annual miles) should receive higher monetary value than for mobility-rich (high annual mile travelers), because accessibility-constrained people tend to gain relatively more from a given transportation improvement. This means that travel time savings for mobility-poor people should be valued higher than for the mobility-rich. This helps increase consumer welfare and efficiency, not just social justice objectives. For example, it helps disadvantaged people access education and employment opportunities that allow them to be more productive.
The New Jersey Department of Transportation developed an interactive GIS-based tool for calculating network-wide full marginal costs (FMC) of highway transportation in New Jersey. This tool is used to evaluate the short-term impacts of policy implications on the marginal costs of different trips. Application of this model on a sample network shows that the “traditional” distance-based approach overestimates the marginal cost of the network, and more importantly it provides marginal cost on the basis of distance rather than trips, which is the most basic way of considering travel behavior of drivers. Results obtained from application of this model on the North Jersey network demonstrate that FMC between an Origin-Destination (O-D) pair exhibit differences among various paths that connect any single O-D pair. These results also demonstrate the importance of analyzing trips based on a number of factors in addition to travel times such as volume, capacity, road type, and distance. The analyses conducted to observe the short-term impacts of capacity investments on several route sections (NJ Route 18, NJ Route 17, NJ Route 3, and the Garden State Parkway) demonstrate that even though capacity investments can reduce the marginal cost of users, the amount of savings mainly depends on the characteristics of that region, the excessive demand that needs to be satisfied, and the reduced congestion delays. This will help planners to estimate the changes in transport costs due to a particular transportation demand management measure or supply change such as adding new lanes or improving existing lanes.
Peter Furnish and Don Wignall (2009) review the role of conventional transport models in the development of transport policies and strategies, and explore the additional contribution that simplified models could make, if these were made more widely available. An example of a simplified model is described to illustrate the use of this type of modelling for policy and strategy development purposes. The paper concludes that conventional transport models and simplified models could play greater roles in supporting the development of transport policies and strategies.
PECAS is an integrated urban model of California being developed by a team from the University of California, Davis and the University of Calgary. It is a spatial economic urban model, using zones and a network-based travel model, which gives a theoretically valid measure of modelwide (regional or statewide) utility. PECAS combines the concepts from traditional Walrasian (general equilibrium) economics with random utility theory. Random utility theory permits the representation of heterogeneous goods and actors with heterogeneous tastes, with prices for goods varying by zone. Also, the implementation of discrete choice theory using logit equations permits partial utility to be represented, which is useful in welfare analysis of alternative goods and locations. This model structure gives utility measures for households and for firms, both as producers and as consumers. The nested logit overall structure of PECAS also gives a regionwide or statewide utility measure, useful for aggregate welfare evaluation.
The California model set will produce typical measures of transportation system performance such as VMT, delay, mode shares, and congestion. These measures can be broken out by trip purpose and by household income class. The models will also give a broad array of outputs representing economic efficiency for households and firms, economic equity for households, housing rents, and housing affordability for households by income class (monthly rent/monthly income). It will also produce measures concerning changes in natural resources, such as amount of land converted from agriculture and grazing or from various habitat types to urban and suburban development. Related environmental impact measures will include energy use, on-road air pollution, and greenhouse gases, plus basic measures of water quality at various watershed levels.
This model set should be useful for a variety of public policy analysis. Single-purpose state and federal agencies can apply measures that relate to the issues within their jurisdiction. For example, a housing agency can evaluate impacts on housing affordability, while transportation agencies can evaluate traffic delay, congestion, and pollutant emissions. An energy agency can analyze the impacts of various transportation scenarios on energy use, and the cost effectiveness of energy conservation and greenhouse emission reduction strategies. Natural resources agencies can model pollutant emissions, habitat conversion, erosion potential of developed lands, and water quality from various transport and land use policies.
The SENSEable City Laboratory at the Massachusetts Institute of Technology is using mobile telephone location data available from telephone companies (usually for a fee) to track personal travel patterns, including hourly and daily traffic flows by all modes. This can provide more detailed information than available from conventional travel surveys. Because individuals’ locations are not tracked there is no threat to privacy.
The Transport Emissions Evaluation Models for Projects (TEEMP) is a set of Excel-based models designed to evaluate the emissions impacts of Asian Development Bank’s transport projects (www.adb.org/Documents/Evaluation/Knowledge-Briefs/REG/EKB-REG-2010-16/default.asp). These models were developed by the Clean Air Initiative for Asian Cities (www.cleanairinitiative.org), the Institute for Transportation and Development Policy (www.itdp.org), and Cambridge Systematics for the for Global Environmental Facility (www.thegef.org) Scientific and Technical Advisory Panel (STAP). The Manual for Calculating Greenhouse Gas Benefits of Global Environmental Facility Transportation Projects (www.thegef.org/gef/GEF_C39_Inf.16_Manual_Greenhouse_Gas_Benefits) provide step-by-step instructions for developing baseline and impact estimations for various types of transport policies and projects, including transport efficiency improvement, public transport, non-motorized transport, transport demand management, and comprehensive transport strategies.
The report, Model-based Transportation Performance: A Comparative Framework and Literature Synthesis, incorporates various performance indicators into transportation modeling to evaluate the effectiveness of various land-use, transit, and automobile pricing policies. The results indicate the direction and relative magnitude of change resulting from these policies, as well as potential biases that result in analyses that overlook some of these impacts. Table 7 summarizes the performance indicators used in this modeling.
Table 7 Performance Indicator Framework (Rodier and Spiller 2012)
|
|
Performance Indicator |
Required Model Data |
|
Travel |
Access |
Travel time/cost by origin/destination location, mode, area (corridor, subarea, region), time of day (peak and off-peak), and/or activity type (work, school, shop) |
|
|
Proximity |
Quantity of land consumed; redevelopment and/or infill by type, area, and/or location; total jobs by total households by area |
|
|
Choice |
Transit, pedestrian, and bicycle mode share by area |
|
|
Congestion |
Vehicle speed/distance by mode (including trucks), activity type, area (key corridors or economic destinations) |
|
Equity |
Access |
Access by socioeconomic group and location |
|
|
Spatial |
Clustering of socioeconomic groups by location |
|
|
Housing |
Home location change attributed to rent increase by socioeconomic group |
|
|
Housing |
Supply and cost (rent/own) by type and location |
|
Economic |
Financial/land use |
Built-form input to service cost, tax, and/or infrastructure cost model |
|
|
Financial/transport |
Use and revenue relative to capital and operation and maintenance (O&M) costs |
|
|
Surplus |
Spatial economic effects (producer and consumer surplus) |
|
Environmental |
Energy/climate/air |
Vehicle activity in fuel use, climate change, and emissions models |
|
|
Noise |
Residential location and vehicle facilities in noise models |
|
|
Habitat/ecosystem/ water |
Land consumed by type and location input to habitat, ecosystem, and water models |
This table summarizes performance indicators incorporated in transport models for more comprehensive analysis of impacts of various policy and planning options.
The Technical Advisory on Evaluating Transportation Impacts in CEQA (GOPR 2018) provides guidance for evaluating vehicle travel impacts of specific developments and transportation projects in California in order to be consistent with the state’s laws and policies which favor transportation and land use developments which reduce vehicle miles traveled (VMT).
The study, An Assessment of Urban Form and Pedestrian and Transit Improvements as an Integrated GHG Reduction Strategy, by the Washington State Department of Transportation (www.wsdot.wa.gov/research/reports/fullreports/765.1.pdf) evaluates the effects of various urban form factors on vehicle travel and carbon emissions. It found that increasing sidewalk coverage from a ratio of 0.57 (the equivalent of sidewalk coverage on both sides of 30% of all streets) to 1.4 (coverage on both sides of 70% of all streets) was estimated to result in a 3.4% decrease in VMT and a 4.9% decrease in CO2. Land use mix had a significant association with both CO2 and VMT at the 5 percent level. Parking cost had the strongest associations with both VMT and CO2. An increase in parking charges from approximately $0.28 per hour to $1.19 per hour (50th to 75th percentile), resulted in a 11.5% decrease in VMT and a 9.9% decrease in CO2. However, the required data were only available in more urbanized communities which limited the analysis.
Based on the study results, the research team developed and tested a spreadsheet tool
to estimate the potential reduction in CO2 and VMT due to urban form, sidewalk coverage, transit service and travel cost changes suitable for neighborhood and regional planning. This tool was applied in two Seattle neighborhoods – Bitter Lake and Rainier Beach. Rainier Beach is the location of a new light rail (LRT) stop, while Bitter Lake is along a forthcoming bus rapid transit (BRT) service corridor, and both have a large degree of potential to transition into more walkable, transit supportive areas in the future. The results indicate that current policy will produce small decreases in VMT and CO2: a nearly 8% decrease in VMT, and a 1.65% decrease in CO2 for Bitter Lake; and a 6.75% decrease in VMT and a 2.2% decrease in CO2 for Rainier Beach. This indicates that more investment in pedestrian infrastructure and transit service will almost certainly be needed in order to meet VMT and CO2 reduction targets. A scenario was developed that was focused on VM2 / CO2 reduction – complete sidewalk coverage, decreases in transit travel time and cost, and increases in parking costs, and slight adjustments to the mix of land uses. In total, these changes resulted in a 48% VMT reduction and a 27.5% CO2 reduction for Bitter Lake, and a 27% VMT reduction / 16.5% CO2 reduction for Rainier Beach – substantial departures from the trend that begin to illustrate what might have to happen in order to reach stated goals for VMT reduction.
The open source geo-spatial UrbanFootprint model is being developed and deployed across California’s major regions as part of the Vision California process, and is designed to deploy in regions and jurisdictions across California and the United States. Built by Calthorpe Associates on a base of open source software (i.e. Linux, PostGIS, and PostGreSQL), it is a powerful and dynamic scenario creation and modeling tool with full co-benefits analysis capacity. UrbanFootprint is more than just a land use sketch model – it is a complete data, scenario, and analysis ecosystem, serving as a practical organizing vessel for large and varied data sets, future plan and scenario data, modeling engines, and results reporting. Its thin-client web-based interface requires no proprietary software to run and is designed to run on virtually all operating systems, desktop, and mobile environments. The model currently includes:
· A full set of 30+ detailed and researched place types built up from a set of 50+ building types, each one mixed from three to over a dozen actual real-world built or planned buildings
· A complete 5.5-acre loaded grid for the entire California study area, with the ability to scale down to the parcel level for both scenario creation and analysis
· A scenario translation engine that converts regional and local land use plans into UrbanFootprint placetypes
· A web-based scenario ‘painter’ that allows for custom built scenarios and scenario editing
· An 8-D travel sketch travel model to accurately assess the impact of changes to urban form on travel behavior
· A public health analysis engine that estimates a complete range of active transportation metrics
· Climate-sensitive building energy and water modeling
· Fiscal impacts analysis
· Greenhouse gas and other emissions modeling
UrbanFootprint results are calculated for both the base (existing) year as well as for future scenarios, so as to capture the effect of changes in future urban form on existing areas. The power and speed with which UrbanFootprint operates allows it to undertake much more sophisticated geographical analyses than previous generations of GIS-based sketch models. The model can be used to create and test scenarios at the statewide, regional, county, jurisdictional, neighborhood or single-development scales.
The RapidFire model is a user-friendly, spreadsheet-based tool that is used to produce and evaluate statewide, regional, and county-level scenarios. It emerged out of a near-term need for a comprehensive modeling tool that could inform state, regional, and local agencies and policy makers in evaluating climate, land use, and infrastructure investment policies. The model produces results for a range of critical metrics, including:
· Land consumption
· Infrastructure cost (including capital and operations & maintenance (O&M))
· City/jurisdictional revenues
· Vehicle miles traveled (VMT) and fuel consumption
· Transportation GHG and air pollutant emissions
· Building energy and water consumption and related GHG emissions
· Household costs for transportation and utilities
· Public health (air pollution-related) impacts and costs
Results are calculated using empirical data and the latest research. The model constitutes a single framework into which research-based assumptions can be loaded to test the impacts of varying land use patterns. The transparency of the model’s structure of input assumptions makes it readily adaptable to different study areas, as well as responsive to data emerging from ongoing technical analyses by state and regional agencies. The model can be used to create and test scenarios at the national, statewide, regional, county, and jurisdictional scales.
A comprehensive study concerning methods for evaluating land use impacts on vehicle travel identified three general methodologies that can increase the sensitivity of transport models to smart growth factors:
These approaches are based on assessing the impact of the following four D’s on reducing vehicle trips and vehicle miles traveled: (1) Density, which refers to population and employment per square mile; (2) diversity, which refers to the ratio of jobs to population; (3) design which pertains to aspects of the pedestrian environment design such as street grid density, sidewalk completeness, and route directness; and (4) Destinations, which refers to accessibility compared to other activity concentrations. Calculating the likely reduction in vehicle trips and VMT is based on elasticity factors. Among the more well-known of the post-processing approaches are two GIS-based programs, INDEX and IPLACE3S, which have been used in land-use planning exercises to assess or demonstrate the transportation benefits of alternative smart-growth strategies, particularly in California, as well as elsewhere.
The four-step travel forecasting method is by far the most popular planning method currently in use by metropolitan planning organizations to evaluate alternative land use and transportation developments. However, as identified by many researchers, a variety of issues associated with the process limit its applicability to smart growth practices. Loudon and Parker (2008) identified more than ten limitations of the four-step process in relation to smart growth strategy evaluation. These include: (1) no explicit modeling of trip chaining; (2) a focus primarily on vehicle trips only; (3) limited or no modeling capability for transit, walking and bicycling; (4) fixed vehicle trip rates by land-use type regardless of the design of the development; (5) zonal aggregation of traveler characteristics; (6) large traffic analysis zones; and (7) a focus on primarily modeling peak periods. Moreover, (Greenwald 2006) argued that the traditional four-step model processes do not capture the increase in shorter intra-zonal automobile trips, bicycle trips and walking trips that are encouraged by smart-growth strategies, due to the limitations of the four-step process in modeling intra-zonal trips and travel made by means other than automobiles.
To fulfil the needs for methods and models sensitive enough to smart growth strategies, an enhanced travel demand forecasting framework was developed in this research, which offers an increased sensitivity to the impact of smart growth strategies. There were two reasons that motivated the study to focus on to developing the enhanced travel demand forecasting method, in addition to the post-processor methods mentioned above. First, traditional four-step method can be easily adopted by the MPOs due to historical reasons. And secondly, compared with activitybased models, the enhanced travel demand forecasting approach consumes less data in model calibration and validation, thus saves money.
The enhanced travel demand forecasting approach uses a set of purpose-specific zone-based linear regression functions to quantify trip generation. Six trip purposes are considered, including Home-Based-Work (HBW), Home-Based-Shop (HBShop), Home-Based-Social-Recreation (HBSR), Home-Based-Other (HBO), Non-Home-Based-Work (NHBW), and Non-Home-Based-Other (NHBO). Trip generation gives the total number of trips produced by each TAZ, and then trip synthesizer uses some algorithm to generate the socioeconomic variables for each of the produced trips.
In trip distribution, the traditional gravity model is substituted by five trip-based disaggregate destination choice models, including models for HBW, HBShop, HBSRO (combined by HBSR and HBO), NHBW, and NHBO trips. Mode choice models are two nested logit models, one for intrazonal trips and the other for interzonal trips. Both models consider six alternatives modes, including non-motorized modes (walk and bike). Mode choice could split the overall OD table into six sub OD tables, one for each travel mode. In trip assignment, we used All-or-Nothing method for auto OD table, for illustration. Land use variables are incorporated in three steps: trip generation, destination choice and mode choice. Intrazonal trips are paid special attention to in the enhanced four steps model. Destination choice model takes intrazonal trip as a separate alternative, and intrazonal trips’ mode choice is estimated by a separate mode choice model.
Disaggregate, activity-based models offer the highest level of sensitivity in terms of modeling the impact of smart growth strategies. They are, however, the most complex, and most demanding in terms of implementation cost and effort. In this study, this approach was not investigated in great details because: (1) the approach is very demanding in terms of the data needed for implementation, calibration and validation; and (2) the enhanced four-step modeling framework developed in this study actually captures many of the features and advantages of the activity-based modeling approach. Specifically, many aspects of our modified four-step method are disaggregate in nature and captures individual-level behavior.
San Francisco TDM Tool (www.sftdmtool.org), and Step-by-Step Instructions for Creating a TDM Plan (http://sf-planning.org/shift-transportation-demand-management-tdm), provides specific guidance for transportation planners and engineers, developers and building managers to encourage more efficient transportation for various land use types and conditions. It gives points for various TDM strategies that can be used to evaluate program effectiveness.
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, against a range of background scenarios which take into account the major factors affecting future patterns of travel.
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Recognizing that current transportation models are not sensitive to many transportation demand management (TDM) strategies, the Washington State Department of Transportation developed the Transportation Demand Management Assessment Procedure (TDMAP), as a sketch planning modeling approach to incorporate TDM into WSDOT’s travel demand model. TDMAP does so by (1) extracting mode split tables from the model; (2) processing them to be compatible with TRIMMS 2.0, an existing tool that estimates changes in travel behavior as a result of implementing different TDM strategies; (3) running the tables through TRIMMS 2.0; and then (4) processing them back into the four-step model for distribution over the transportation network. This provides a low cost method to help WSDOT plan TDM strategies as part of its overall transportation planning process. Ideally, the next generation would also help WSDOT identify and choose the most cost-effective mix of program elements for improving traffic and air quality conditions in a corridor, and for the desired level of change in vehicle traffic, and see how the cost and mix varies with the desired level of change. Building such a tool requires additional research, and improved data on the cost and effectiveness of TDM program elements under different conditions. Recommendations for research and data development to help bring this goal to fruition were made. |
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.
Steve Abley and S. Turner (2011), Predicting Walkability, Research Report 452, New Zealand Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/452/docs/452.pdf.
Austroads Project Evaluation Guides (www.onlinepublications.austroads.com.au/collections/agpe/guides) provides information on transport project evaluation methods for use in Australia and New Zealand.
Alain Bertaud Website (http://alain-bertaud.com) includes information on urban models.
Keith Bartholomew and Reid Ewing (2009), “Land Use-Transportation Scenarios and Future Vehicle Travel and Land Consumption: A Meta-Analysis,” Journal of the American Planning Association, Vol. 75, No. 1, Winter (http://dx.doi.org/10.1080/01944360802508726).
Joseph Broach, et al (2017), Walk, Don’t Run? Advancing the State of the Practice in Pedestrian Demand Modeling, Portland State University (www.pdx.edu); slideshow at https://bit.ly/2JjwNeD.
Eric Christian Bruun (2014), Better Public Transit Systems: Analyzing Investments and Performance, Earthscan (www.routledge.com/sustainability).
Calthorpe Associates (2010), The Role of Land Use in Reducing VMT and GHG Emissions:
A Critique of TRB Special Report 298, Calthorpe Associates (www.calthorpe.com);
at www.calthorpe.com/files/TRB-NAS%20Report%20298%20Critique_0.pdf.
Calthorpe Associates (2012), Urban Footprint and Rapidfire Models, Calthorpe Associates (www.calthorpe.com); at www.calthorpe.com/scenario_modeling_tools.
CalTrans (2006), Benefit-Cost Models, Office of Transportation Economics, California Department of Transportation (www.dot.ca.gov/hq/tpp/offices/eab); at www.dot.ca.gov/hq/tpp/offices/eab/LCBC_Analysis_Model.html.
Cambridge Systematics (2009), Performance Measurement Framework for Highway Capacity Decision Making, Strategic Highway Research Program (SHRP) Report S2-C02-RR, TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/shrp2/shrp2_S2-C02-RR.pdf.
CARB (2010-2017), Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).
CCAP (2005), Transportation Emissions Guidebook: Land Use, Transit & Travel Demand Management, Center for Clean Air Policy (www.ccap.org/trans.htm).
Chun-Hung Peter Chen and George A. Naylor (2011), “Development of a Mode Choice Model for Bus Rapid Transit in Santa Clara County, California,” Journal of Public Transportation, Vol. 14, No. 4 (www.nctr.usf.edu); at www.nctr.usf.edu/wp-content/uploads/2011/12/JPT14.4.pdf.
Kelly J. Clifton, et al. (2015), Development of a Pedestrian Demand Estimation Tool, National Institute for Transportation and Communities (http://ppms.otrec.us); at http://ppms.otrec.us/media/project_files/NITC-RR-677_Final_Report.pdf.
Kelly J. Clifton, et al. (2016), “Representing Pedestrian Activity in Travel Demand Models: Framework and Application,” Journal of Transport Geography, Vo. 52, pp. 111–122; summary at www.sciencedirect.com/science/article/pii/S0966692316301302.
Sisinnio Concas and Philip L. Winters (2007), Economics of Travel Demand Management: Comparative Cost Effectiveness and Public Investment, Center for Urban Transportation Research (www.nctr.usf.edu); at www.nctr.usf.edu/pdf/77704.pdf.
COST (2014), Assessing Usability of Accessibility Instruments, COST Accessibility Planning Program (www.accessibilityplanning.eu), European Cooperation in Science and Technology; at www.accessibilityplanning.eu/reports/report-2.
DfT (2010), National Transport Model, Integrated Transport Economics and Appraisal, Department for Transport (www.dft.gov.uk); at www.dft.gov.uk/pgr/economics/ntm.
DfT (2013), Transport Analysis Guidance: WebTAG, Integrated Transport Economics and Appraisal, Department for Transport (www.dft.gov.uk); at www.gov.uk/guidance/transport-analysis-guidance-webtag. This website provides guidance on methods for evaluating TDM programs such as workplace travel plans, school travel plans, personalised travel planning, travel awareness campaigns, public transport information and marketing, car clubs, car sharing schemes, teleworking, teleconferencing, and home shopping.
Distillate (www.distillate.ac.uk) (Design and Implementation Support Tools for Integrated Local LAnd use, Transport and the Environment) is a research project to help overcoming the barriers to the effective development and delivery of sustainable urban transport and land use strategies.
Richard Dowling, et al. (2008), Multimodal Level Of Service Analysis For Urban Streets, NCHRP Report 616, Transportation Research Board (www.trb.org); at http://trb.org/news/blurb_detail.asp?id=9470; User Guide at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w128.pdf.
Dowling Associates (2009), Application of Microsimulation in Combination With Travel Demand Models, Applying Analysis Tools in Planning for Operations Case Study #4, Federal Highway Administration Office of Operations (www.ops.fhwa.dot.gov); at www.ops.fhwa.dot.gov/publications/fhwahop10005/fhwahop10005.pdf.
Dowling Associates (2010), CompleteStreets LOS: Multi-Modal Level-of-Service Toolkit, Dowling Associates (www.dowlinginc.com/completestreetslos.php). This software program automates the procedures described in NCHRP Report 616, Multimodal Level of Service for Urban Streets, for evaluating complete streets, context-sensitive design alternatives, and smart growth from the perspective of all users of the street.
Economic Methods and Studies Website (https://ops.fhwa.dot.gov/freight/pol_plng_finance/economics/index.htm) Office of Freight Management and Operations, Federal Highway Administration.
David Ellis, Brianne Glover and Nicolas Norboge (2012), Refining a Methodology for Determining the Economic Impacts of Transportation Improvements, University Transportation Center for Mobility at Texas A&M University (http://utcm.tamu.edu) for the U.S. Department of Transportation ; at http://utcm.tamu.edu/publications/final_reports/Ellis_11-00-68.pdf.
Reid Ewing, et al. (2007), Growing Cooler: The Evidence on Urban Development and Climate Change, Urban Land Institute and Smart Growth America (www.smartgrowthamerica.org/gcindex.html).
Reid Ewing, et al. (2011), “Traffic Generated by Mixed-Use Developments – A Six-Region Study Using Consistent Built Environmental Measures,” Journal of Urban Planning and Development, American Society of Civil Engineers (DOI: 10.1061/(ASCE)UP.1943-5444.0000068); draft at http://bit.ly/2pt298R.
Reid Ewing, et al. (2017), Trip and Parking Generation Study of Orenco Station TOD, Portland Region, NITC-RR-767, Transportation Research and Education Center (TREC); at https://doi.org/10.15760/trec.157.
Lawrence D. Frank , et al. (2011), An Assessment of Urban Form and Pedestrian and Transit Improvements as an Integrated GHG Reduction Strategy, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/research/reports/fullreports/765.1.pdf.
Peter Furnish and Don Wignall (2009), Making the Most of Models: Using Models To Develop More Effective Transport Policies And Strategies, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/FerWig_Modelling.pdf.
Shengyi Gao, et al. (2009), Developing the California Integrated Land Use/Transportation Model, Research Report UCD-ITS-RR-09-30, Institute of Transportation Studies, University of California, Davis (www.its.ucdavis.edu); at www.its.ucdavis.edu/research/publications/publication-detail/?pub_id=1334.
Eric J. Gonzales, Celeste Chavis, Yuwei Li and Carlos F. Daganzo (2009), Multimodal Transport Modeling for Nairobi, Kenya: Insights and Recommendations with an Evidence-Based Model, Working Paper UCB-ITS-VWP-2009-5, UC Berkeley Center for Future Urban Transport; at www.its.berkeley.edu/publications/UCB/2009/VWP/UCB-ITS-VWP-2009-5.pdf.
GOPR (2018), Technical Advisory on Evaluating Transportation Impacts in CEQA, Governor’s Office for Planning and Research, State of California (http://opr.ca.gov); at http://opr.ca.gov/docs/20181228-743_Technical_Advisory.pdf.
Susan Handy, Gil Tal and Marlon G. Boarnet (2014), Policy Brief on the Impacts of Bicycling Strategies Based on a Review of the Empirical Literature, for Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).
David Hensher (2002), “A Systematic Assessment of the Environmental Impacts of Transport Policy,” Environment and Resource Economics, Vol. 22, Part 1, pp. 185-217; at https://link.springer.com/article/10.1023/A:1015527601997.
Jill Luria Hough and Gary Black (2012), “Effective Travel Demand Modeling to Support Smart Growth and Climate Change Policies,” ITE Journal (www.ite.org), Vol. 82, No. 5, May, pp. 30-35; at www.ite.org/membersonly/itejournal/pdf/2012/JB12EA30.pdf.
Michael Iacono, Kevin Krizek and Ahmed El-Geneidy (2008), Access to Destinations: How Close is Close Enough? Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes, Report 2008-11, University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=916.
ICF International (2009), Benefit/Cost Analysis Of Converting A Lane For Bus Rapid Transit, Research Results Digest 336, National Cooperative Highway Research Program, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rrd_336.pdf; summary at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rrd_352.pdf.
ITDP (2010), Manual for Calculating Greenhouse Gas Benefits of Global Environmental Facility Transportation Projects, Institute for Transportation and Development Policy, for the Scientific and Technical Advisory Panel of the Global Environment Facility (www.thegef.org); at www.thegef.org/gef/GEF_C39_Inf.16_Manual_Greenhouse_Gas_Benefits.
Robert Johnston (2004), UPlan: A Versatile Urban Growth Model for Transportation Planning, University of California at Davis (www.des.ucdavis.edu/faculty/johnston/pubs.htm)
Robert Johnston (2006), Review of U.S. and European Regional Modeling Studies of Policies Intended to Reduce Motorized Travel, Fuel Use, and Emissions, Environmental Science & Policy, University of California, Davis; at www.vtpi.org/johnston.pdf.
Robert A. Johnston (2008), “Indicators for Sustainable Transportation Planning,” Transportation Research Record 2067, Transportation Research Board (www.trb.org), pp. 146 – 154; at http://pubs.its.ucdavis.edu/publication_detail.php?id=1260.
Jacob Koch, Luis Antonio Lindau and Carlos David Nassi (2013), Transportation in the Favelas of Rio de Janeiro, Lincoln Institute (www.lincolninst.edu); at www.lincolninst.edu/pubs/2231_Transportation-in-the-Favelas-of-Rio-de-Janeiro.
Kevin Krizek, et al. (2007), Access to Destinations: Refining Methods for Calculating Non-Auto Travel Times, Report No. 2, Access to Destinations Study, University of Minnesota's Center for Transportation Studies (www.cts.umn.edu/access-study/publications).
J. Richard Kuzmyak (2012), Land Use and Traffic Congestion, Report 618, Arizona Department of Transportation (www.azdot.gov); at www.azdot.gov/TPD/ATRC/publications/project_reports/PDF/AZ618.pdf.
J. Richard Kuzmyak, et al. (2014), Estimating Bicycling and Walking for Planning and Project Development: A Guidebook, NCHRP Report 770, Transportation Research Board (www.trb.org); at www.trb.org/main/blurbs/171138.aspx.
Richard Lee, et al. (2012), “Evaluation of the Operation and Accuracy of Five Available Smart Growth Trip Generation Methodologies,” Transportation Research Record 2307, (DOI: 10.3141/2307-13); at http://trrjournalonline.trb.org/doi/abs/10.3141/2307-13. Also see, Annotated Literature Review on Land Use-Transportation Relationships, prepared for CalTrans, January 2012; at http://bit.ly/2jNm8kP.
Jonathan Levine, Joe Grengs, Qingyun Shen and Qing Shen (2012), “Does Accessibility Require Density or Speed?” Journal of the American Planning Association, Vol. 78, No. 2, pp. 157-172, http://dx.doi.org/10.1080/01944363.2012.677119; at www.connectnorwalk.com/wp-content/uploads/JAPA-article-mobility-vs-proximity.pdf.
David Levinson (2013), Access Across America, Report 13, Access to Destinations Study, Center for Transportation at the University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=2280.
Todd Litman (2001), “Generated Traffic; Implications for Transport Planning,” ITE Journal, Vol. 71, No. 4, Institute of Transportation Engineers (www.ite.org), April, 2001, pp. 38-47; at www.vtpi.org/gentraf.pdf.
Todd Litman (2004), “Transit Price Elasticities and Cross-Elasticities,” Journal of Public Transportation, Vol. 7, No. 2, (www.nctr.usf.edu/jpt/pdf/JPT 7-2 Litman.pdf), pp. 37-58; an updated version is at www.vtpi.org/tranelas.pdf.
Todd Litman (2004b), What’s It Worth? Life Cycle and Benefit/Cost Analysis for Evaluating Economic Value, Presented at Internet Symposium on Benefit-Cost Analysis, Transportation Association of Canada (www.tac-atc.ca); at www.vtpi.org/worth.pdf.
Todd Litman (2005), Transportation Cost and Benefit Analysis Guidebook, Victoria Transport Policy Institute (www.vtpi.org/tca).
Todd Litman (2006), Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/elasticities.pdf .
Todd Litman (2007a), Build For Comfort, Not Just Speed: Valuing Service Quality Improvements In Transport Planning, VTPI (www.vtpi.org); at www.vtpi.org/comfort.pdf.
Todd Litman (2007b), Comprehensive Transport Planning, VTPI (www.vtpi.org); at www.vtpi.org/comprehensive.pdf.
Todd Litman (2008), Land Use Impacts on Transport: How Land Use Factors Affect Travel Behavior, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/landtravel.pdf; originally published as “Evaluating Transportation Land Use Impacts,” World Transport Policy & Practice, Vol. 1, No. 4, 1995 pp. 9-16; at www.eco-logica.co.uk/pdf/wtpp01.4.pdf.
Todd Litman (2009), Evaluating Accessibility for Transportation Planning, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/access.pdf .
Todd Litman (2013), “The New Transportation Planning Paradigm,” ITE Journal (www.ite.org), Vo. 83, No. 6, pp. 20-28; at http://digitaleditions.sheridan.com/publication/?i=161624.
Todd Litman (2013), Congestion Costing Critique: Critical Evaluation of the ‘Urban Mobility Report,’ VTPI (www.vtpi.org); at www.vtpi.org/UMR_critique.pdf.
Todd Litman (2014), Toward More Comprehensive and Multi-modal Transport Evaluation, VTPI (www.vtpi.org); at www.vtpi.org/comp_evaluation.pdf; summarized in JOURNEYS, September 2013, pp. 50-58; at www.lta.gov.sg/ltaacademy/doc/13Sep050-Litman_ComprehensiveAndMultimodal.pdf.
Todd Litman (2017), Reduced and More Accurate Parking Requirements, Planetizen (www.planetizen.com); at www.planetizen.com/node/92360/reduced-and-more-accurate-parking-requirements.
William Loudon and Terry Parker (2008), Modeling the Travel Impacts of Smart-Growth Strategies, Transportation Research Board 87th Annual Meeting (www.trb.org).
Darshini Mahadevia, Rutul Joshi and Abhijit Datey (2013), Low-Carbon Mobility in India and the Challenges of Social Inclusion: Bus Rapid Transit (BRT) Case Studies in India, CEPT University Centre for Urban Equity (http://cept.ac.in/178/center-for-urban-equity-cue-), United Nations Environmental Program; at www.unep.org/transport/lowcarbon/Pdf's/BRT_Casestudies_India_fullreport.pdf.
Karel Martens (2006), “Basing Transport Planning on Principles of Social Justice,” Berkeley Planning Journal, Volume 19 (www-dcrp.ced.berkeley.edu/bpj).
Michael West Mehaffy (2015), Urban Form and Greenhouse Gas Emissions Findings, Strategies, and Design Decision Support Technologies, Delft University of Technology (http://abe.tudelft.nl); at http://abe.tudelft.nl/index.php/faculty-architecture/article/view/1092/pdf_mehaffy.
Adam Millard-Ball (2015), “Phantom Trips: Overestimating the Traffic Impacts of New Development,” Journal of Transportation and Land Use (www.jtlu.org); at http://tinyurl.com/m6ay4ut; summarized in, ACCESS 45, pp. 3-8; at www.accessmagazine.org/articles/fall-2014/phantom-trips.
NCHRP (2010), Advanced Practices in Travel Forecasting, Synthesis 406, National Cooperative Highway Research Program, TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_406.pdf.
NJDOT (2007), Cost of Transporting People in New Jersey, New Jersey Department of Transportation and the Region 2 University Transportation Research Center, U.S. Department of Transportation; at www.nj.gov/transportation/refdata/research/reports/FHWA-NJ-2007-003.pdf.
OTREC (2009), Co-Evolution of Transportation and Land Use: Modeling Historical Dependencies in Land Use and Transportation Decision Making, OTREC-RR-09-08, Oregon Transportation Research and Education Consortium (www.otrec.us); at www.otrec.us/project/68.
Lee Pike (2011), Generation of Walking, Cycling and Public Transport Trips: Pilot Study, New Zealand Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/439/docs/439.pdf.
Richard Pratt, et al (2012), Pedestrian and Bicycle Facilities, Chapter 16, Traveler Response to Transportation System Changes, TCRP Report 95, TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_95c16.pdf.
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/dmg/03; 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.
Caroline Rodier (2009), “A Review of the International Modeling Literature: Transit, Land Use, and Auto Pricing Strategies to Reduce Vehicle Miles Traveled and Greenhouse Gas Emissions,” Transportation Research Record 2132, TRB (www.trb.org), pp. 1-12; at www.worldtransitresearch.info/research/92.
Caroline Rodier and Margot Spiller (2012), Model-based Transportation Performance: A Comparative Framework and Literature Synthesis, Report 11-09, Mineta Transportation Institute (www.transweb.sjsu.edu); at www.transweb.sjsu.edu/PDFs/research/2805-Model-based-transportation-performance.pdf.
RPA (2014), “Access to Jobs,” Fragile Success, Regional Plan Association (www.rpa.org); at http://fragile-success.rpa.org/maps/jobs.html.
Adel W. Sadek, et al. (2011), Reducing Vehicle Miles Traveled through Smart Land-use Design, New York State Energy Research and Development Authority and the New York Department Of Transportation (www.dot.ny.gov); at http://on.ny.gov/2wxW1iR.
San Francisco TDM Tool (www.sftdmtool.org), and Step-by-Step Instructions for Creating a TDM Plan (http://sf-planning.org/shift-transportation-demand-management-tdm), provides specific guidance for transportation planners and engineers, developers and building managers to encourage more efficient transportation.
SFPD (2018), TDM Menu of Options, San Francisco Planning Department (http://sf-planning.org); at http://sf-planning.org/tdm-menu-options.
SANDAG (2012), Integrating Transportation Demand Management into the Planning and Development Process: A Reference for Cities, iCommute (www.icommutesd.com), San Diego Regional Planning and HNTB; at www.icommutesd.com/documents/TDMStudy_May2012_webversion_000.pdf.
Jan Scheurer, Edmund Horan and Shamas Bajwa (2009), Benchmarking Public Transport and Land Use Integration in Melbourne and Hamburg: Hints for Policy Makers, AESOP 2009 Congress, Liverpool; at www.global-cities.info/uploads/0f/98/0f98352056404be3c3b9fc1a41d1cfe7/AESOP-paper-july-2009.pdf.
Robert J. Schneider, Susan L. Handy and Kevan Shafizadeh (2014), “Trip Generation for Smart Growth Projects,” ACCESS 45, pp. 10-15; at http://tinyurl.com/oye8aqj. Also see the Smart Growth Trip-Generation Adjustment Tool (http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation).
School Travel Health Check website (www.schooltravelhealthcheck.co.uk) provides mapping analysis to support school transport management programs, such as the number of pupils within a realistic walking distance that travel by car as well, and travel carbon footprint or calories burned for all journeys to school by all modes of travel.
Gian-Claudia Sciara, Susan Handy and Marlon G. Boarnet (2016), Policy Brief on the Impacts of Pedestrian Strategies Based on a Review of the Empirical Literature, for Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).
Jay S. Shah and Bhargav Adhvaryu (2016), “Public Transport Accessibility Levels for Ahmedabad, India,” Journal of Public Transportation, 19 (3): 19-35 (DOI: http://dx.doi.org/10.5038/2375-0901.19.3.2); at http://scholarcommons.usf.edu/jpt/vol19/iss3/2.
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.
Eric Sundquist, et al. (2018), Modernizing Mitigation: A Demand-Centered Approach, State Smart Transportation Initiative (www.ssti.us) and the Mayors Innovation Project (www.mayorsinnovation.org); at https://bit.ly/2TCxtBD.
Guang Tian, et al. (2015), “Traffic Generated by Mixed-Use Developments: Thirteen-Region Study Using Consistent Measures of Built Environment,” Transportation Research Record 2500, Transportation Research Board (www.trb.org), pp. 116–124, DOI: 10.3141/2500-14; slide show at http://bit.ly/2poIndK.
TCRP (2014), Characteristics of Premium Transit Services that Affect Choice of Mode, Transit Report 166, Cooperative Research Program (TCRP), Transportation Research Board; at www.trb.org/main/blurbs/170601.aspx.
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.
Traffic Analysis Tools Website (www.ops.fhwa.dot.gov/trafficanalysistools/type_tools.htm), Office of Operations, Federal Highway Administration.
Transportation Cost-Savings Calculators (https://mobilitylab.org/calculators) includes the TDM Return on Investment Calculator (TDM ROI) helps users calculate vehicle trips and miles travelled reduced by their TDM programs and to calculate benefit-cost ratios or ROI, and the TRIMMS model 4.0 (Trip Reduction Impacts of Mobility Management Strategies) estimates the impacts of a broad range of TDM initiatives and provides program cost-effectiveness assessment, such as net program benefit and benefit-to-cost ratio analysis at an area-wide or site-specific level.
Travel Forecasting Resource (http://tfresource.org) is an ongoing, volunteer program to provide salient information to those practicing travel demand forecasting.
TRB (2012), The Effect of Smart Growth Policies on Travel Demand, Capacity Project C16, Strategic Highway Research Program (SHRP 2), TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2prepubC16.pdf.
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.
USDOE (2013), Transportation Energy Futures, U.S. Department of Energy (www.fleets.doe.gov); at www1.eere.energy.gov/analysis/transportationenergyfutures.
USEPA (2005), Transportation Emission Models, U.S. Environmental Protection Agency (www.epa.gov/state-and-local-transportation).
Anne Vernez Moudon and Orion Stewart (2013), Tools for Estimating VMT Reductions from Built Environment Changes, WA-RD 806.3, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/research/reports/fullreports/806.3.pdf.
VIBAT (www.vibat.org), Visioning and Backcasting for UK Transport Policy, Bartlett School of Planning, University College London. This research project involves modeling to evaluate various policies to achieve 60% reductions in transportation climate change emission, including new technologies and travel reduction incentives.
Philip L. Winters, et al. (2010), Incorporating Assumptions for TDM Impacts in a Regional Travel Demand Model, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/research/reports/fullreports/746.1.pdf.
Ningsheng Zhou and Paul B. Smith (2012), “Estimated VMT and GHG Emission Reductions Associated with the Going to the River Project,” ITE Journal (www.ite.org), Vol. 82, No. 5, May, pp. 30-35; at www.ite.org/membersonly/itejournal/pdf/2012/JB12EA42.pdf.
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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