University of California Transportation Center

In the two decades since the United States Congress passed the federal Intermodal Surface Transportation Efficiency Act, there has been a surge of interest in making urban transportation systems more sustainable. Many agencies, representing all levels of government, have searched for strategies to reduce private automobile use, including policies to shift local driving to pedestrian and bicycle modes. Progress has been made in a number of communities, but the automobile remains the dominant mode of transportation in all metropolitan regions.

Sustainable transportation advocates are especially interested in routine travel, such as shopping and other errands, because it tends to be done frequently and for distances that could be covered realistically by walking or bicycling. According to the 2009 National Household Travel Survey, Americans made more trips for shopping than for any other purpose, including commuting to and from work. One-third of these shopping trips were shorter than two miles (3.2 km). However, 76% of these short shopping trips were made by automobile, while only 21% were made by walking and 1% by bicycling.

In order to identify effective strategies to change travel behavior, practitioners need a greater understanding of why people choose certain modes for routine travel. Choosing to walk or bicycle rather than travel by automobile may help individuals get exercise, save money, interact with neighbors, and reduce tailpipe emissions. Yet, in some communities, non-motorized modes may also require more time and physical effort to run a series of errands, be less convenient for carrying packages and traveling in bad weather, and be perceived as having a higher risk of traffic crashes or street crime than driving.

A mixed-methods approach was used to develop a more complete understanding of factors that are associated with walking or bicycling rather than driving for routine travel. An intercept survey was implemented to gather travel data from 1,003 customers at retail pharmacy stores in 20 San Francisco Bay Area neighborhoods in fall 2009. Follow-up interviews were conducted with 26 survey participants in spring and summer 2010 to gain a deeper understanding of factors that influenced their transportation decisions.

The methodological approach makes several contributions to the body of research on sustainable transportation. For example, the study: Explored multiple categories of factors that may be associated with walking and bicycling, including travel, socioeconomic, attitude, perception, and shopping district characteristics. Few studies of pedestrian or bicycle mode choices have included all of these categories of factors. Statistical models showed that variables in all categories had significant associations with mode choice. Documented and analyzed short pedestrian movements, such as from a parking space to a store entrance or from a bus stop to home. These detailed data provided a greater understanding of pedestrian activity than traditional travel survey analyses. Walking was used as the primary mode for 65% of respondent trips between stops within shopping districts, and 52% of all respondents walked along a street or between stops at some time between leaving and returning home. Maps of respondent pedestrian path density revealed distinct pedestrian activity patterns in different types of shopping districts. Used four different approaches to capture participant travel mode information. Respondents reported the primary mode of transportation they were using on the day of the survey, the mode they typically used, and all modes that they would consider using to travel to the survey store. They also mapped all stops on their tour and said what modes they used between each stop. These four approaches revealed nuanced travel habits and made it possible to correct inaccuracies in self-reported primary travel mode data. Measured and tested fine-grained local environment variables in shopping districts rather than around respondents' homes. These variables characterized the shopping district area (e.g., sidewalks, bicycle facilities, metered parking, and tree canopy coverage), the main commercial roadway (e.g., posted speed limit, number of automobile lanes, and pedestrian crossing distance), and the survey store site (e.g., number of automobile and bicycle parking spaces and distance from the public sidewalk to the store entrance). This dissertation adds to the small number of studies that have explored how the characteristics of activity destinations are related to travel behavior.

The study results contribute to the body of knowledge about factors that may encourage people to shift routine travel from automobile to pedestrian or bicycle modes. After controlling for travel factors such as time and cost, socioeconomic characteristics, and individual attitudes, mixed logit models showed that automobile use was negatively associated with higher employment density, smaller parking lots, and metered on-street parking in the shopping district. Walking was positively associated with higher population density, more street tree canopy coverage, lower speed limits, and fewer commercial driveway crossings. The exploratory analysis of a small number of bicycle tours found that bicycling was associated with more extensive bicycle facility networks and more bicycle parking. However, people were more likely to drive when they perceived a high risk of crime.

Results also suggest the magnitude of mode shifts that could occur if short- and long-term land use and transportation system changes were made to each study shopping district. The mode choice model representing travel only to and from the study shopping districts (N = 388) was used to estimate respondent mode shares under the following three scenarios: 1) double population and employment densities in each study shopping district, 2) double street tree canopy coverage in each study shopping district, and 3) eliminate half of the automobile parking 3 spaces at the survey store. Based on the model, the combination of these three changes could increase pedestrian mode share among the 388 sample respondents from 43% to 61% and decrease automobile mode share from 50% to 31%. This shift could eliminate 129 (13%) of the 983 respondent vehicle miles traveled (208 of the 1,580 respondent vehicle kilometers traveled), and 110 (36%) of the 308 times respondents parked their automobiles in the shopping district.

The mode choice model of walking versus driving within survey shopping districts (N = 286) was used to test the combination of the following scenarios: 1) cluster separated stores around shared parking lots, 2) consolidate commercial driveways so that there are half as many driveway crossings along the main commercial roadway, 3) reduce all main commercial roadway speed limits to 25 miles per hour (40 kilometers per hour), and 4) install metered parking in all shopping districts. These changes could increase the percentage of the 286 sample respondents walking between shopping district activities from 32% to 54%. This shift could eliminate 29 (38%) of the 76 respondent vehicle miles traveled (47 of the 122 respondent vehicle kilometers traveled), and 105 (22%) of the 469 times respondents parked their automobiles in the shopping district. Note that these forecasted mode shifts are illustrative examples based on cross-sectional data and do not account for the process of modifying travel behavior habits.

Qualitative interviews provided a foundation for a proposed Theory of Routine Mode Choice Decisions. This five-step theory also drew from survey results and other mode choice theories in the transportation and psychology fields. The first step, 1) awareness and availability, determines which modes are viewed as possible choices for routine travel. The next three steps, 2) basic safety and security, 3) convenience and cost, and 4) enjoyment, assess situational tradeoffs between modes in the choice set and are supported by many of the statisticallysignificant factors in the mode choice models. The final step, 5) habit, reinforces previous choices and closes the decision process loop. Socioeconomic characteristics explain differences in how individuals view each step in the process. Understanding each step in the mode choice decision process can help planners, designers, engineers, and other policy-makers implement a comprehensive set of strategies that may be able to shift routine automobile travel to pedestrian and bicycle modes.

Modeling personal travel behavior is complex, particularly when one tries to adhere closely to actual casual mechanisms while predicting human response to changes in the transport environement. There has long been a need for explicitly modeling the underlying determinant of travel- the demand for participation in out-of-home activities; and progress is being made in this area, primarily through discrete-choice models coupled with continous-duration choices. However, these models tend to be restircted in size and conditional on a wide variety of other choices that could be modeled more endogenously. 

Broken sidewalks have become an important legal issue since 2002 when the United States Court of Appeals for the NinthCircuit ruled that the Americans with Disabilities ActADAapplies to sidewalks. As one way to comply with the ADA, cities can requireproperty owners to repair any broken sidewalk fronting their property before they sell the property. Before any real estate is sold, the cityinspects the sidewalk fronting the property. If the sidewalk is in good condition, the city does not require the owner to do anything. If thesidewalk is broken, however, the city requires the owner to repair it before selling the property. Analysis of sales data shows that if LosAngeles had adopted a point-of-sale program in 1995, about half of the city’s 4,600 miles of broken sidewalks would have been repairedby 2007. A walkable city needs walkable sidewalks. Requiring sidewalk repairs when property is sold can help put cities back on theirfeet.

American attitudes toward transportation planning have recently undergone significant change. For three decades after the end of World War II, public policy emphasized the construction of new highway and transit facilities in order to remove the backlog of needs which resulted from the combined effects of depression, a war economy, continued urban growth, and accelerating automobile ownership. For the most part, there was consensus among transportation policymakers that their primary goal was to accommodate growth by constructing facilities which would have adequate capacity to handle future demand. It was understood that land use patterns and economic development were the sources of traffic, yet there was general agreement that transportation policy should aim to accommodate forecast land use and economic growth rather than to regulate them in order to control traffic.

Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the U.S. inventory. To reduce CO2 emissions in the future, transportation policy makers are planning on making vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. Traffic congestion and its impact on CO2 emissions were examined by using detailed energy and emission models, and they were linked to real-world driving patterns and traffic conditions. With typical traffic conditions in Southern California as an example, it was found that CO2 emissions could be reduced by up to almost 20% through three different strategies: congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and shock wave suppression techniques that eliminate the acceleration and deceleration events associated with the stop-and-go traffic that exists during congested conditions.

Just as land-use environments vary throughout suburban America, so should parking policies. Parking ordinances should be more flexible for projects situated near rail stops. Based on our research, for example, developers of relatively dense apartments with adjoining retail shops and short, direct walking connections to rail stations should have the option of supplying fewer parking spaces than the norm. Flexibility might also take the form of unbundling the cost of parking from the cost of renting housing or providing residents with the option of choosing transit eco-passes rather than paying for an on-site space. And in light of the fact that TOD residents were found to commute by transit proportionately more than they shed cars or reduced parking, car-sharing should be provided in as many rail-served neighborhoods as possible. Putting shared-cars in and around TODs could relieve many households from owning a second car or a vehicle altogether, which would result in not only considerably lower trip generation rates, but considerably less parking demand as well.

Most policies to cut the transportation sector’s CO2 emissions focus on fuel-efficient vehicles, low-carbon fuels, and reductions in vehicle-miles traveled. One strategy that gets less attention but has high potential pay-off is Intelligent Transportation Systems (ITS). An example is variable speed limits on freeways, illustrated in the photo below. Under this scheme, motorists are alerted of downstream congestion and the adjusted posted speed limits help maintain a steadier, more even flow. Reducing the amount of stop-and-go traffic can significantly cut down on tailpipe emissions and fuel waste.

Some studies suggest that the carbon reduction benefits of ITS are minimal. For example, the recently released “Moving Cooler” report, prepared by Cambridge Systematics, estimates that ITS would reduce emissions by less than 1% nationwide. Others contend that ITS projects could induce new travel that offset some of the gains. We believe that these analyses fail to include key calculations that cast ITS in a more favorable light.

The San Pedro Bay Port (SPBP) of Los Angeles and Long Beach is the largest container port in the U.S. Although the benefits of handling and hauling freight are enjoyed by the nation as a whole, the traffic congestion and air pollution created by the port falls mostly on the people who live and work nearby and along connecting freight corridors. These corridors include two busy freeways, the I-710 and the I-110, and an active rail link, the Alameda corridor. 

This research studied the environmental and health impacts of freight operations between the SPBP and downtown Los Angeles, some 22 miles to the north. In our analysis of health impacts, we focused on nitrogen oxide (NOX), a contributor to the formation of photo-chemical smog, and fine-grain particulates (PM10), which can lodge in peoples’ lungs with repeated exposure. We combined estimates of air pollutants from the I-710 and I-110 freeways, line-haul rail lines, and rail yards and looked at them for summer and winter. Four models were linked together to assess impacts: a microscopic traffic simulation model (TransModeler), which describes vehicle behavior; an emissions model (EMFAC 2007), which estimates the impacts of congestion on air pollution; a pollutant dispersion model (CALPUFF), which calculates how emissions move in a region’s atmosphere; and a health impact model (BenMAP), which calculates various pollutants’ effects on health using the incidence of various pollution-related illnesses.

In the past ten years, integrated land use and transportation modeling has received considerable attention in the scholarly literature. This academic interest is slowly yielding practical applications. Many metropolitan planning organizations (MPOs) and state departments of transportation are beginning to implement these types of models for the first time. While many improvements have been made to these models, and the value of these improvements should not be understated, much work still remains. One of the most challenging problems in land use modeling is how floorspace (buildings) is built and occupied. The purpose of this paper is twofold: first, to draw attention to insufficiencies in the representation of floorspace developer behavior—particularly as it applies to large, urban-edge projects—within current integrated land use and transportation models and, second, to determine the necessity of explicitly accounting for such projects within these models. The Sacramento MEPLAN model will be used together with historical development records to demonstrate and test these assertions. Single large developments are modeled with a common year of development, size, and location. Among the findings, large developments are fairly common in the Sacramento region and make up a considerable amount of floorspace development in absolute terms, large basic sector developments have more of an impact and are therefore more important to explicitly account for than are large non-basic sector developments. A single large basic sector development modeled in a 20 year forecast has a significant impact on zonal outputs. Recommendations are put forward regarding the use of this research in practical modeling exercises.

With modern multivariate statistical methods and activity-diary (time-use) data sets, it is possible to model household mobility decisions as being derived from decisions to participate in activities at various locations. We show how this can be accomplished by specifying activity participation by activity type and location as endogenous variables, with a simple locational distinction of “at home” versus “out of home.” The activity participation variables are then combined in a model system of simultaneous equations with variables that measure mobility demand: travel times by mode, household vehicle ownership and household vehicle utilization. We specify the model in terms of latent, multivariate normally distributed choice variables, and this treatment solves estimation problems associated with censored and ordinal observed endogenous variables. The estimation method provides accurate goodness-of-fit model evaluation and hypothesis testing. Results are shown from a model estimated using two-day activity diary data for male and female household heads and associated accessibility data collected in the Portland, Oregon, U.S.A. Metropolitan Area in 1994. The model system can be used in conjunction with conventional travel demand models, to provide forecasts of the effects of factors such as accessibility and in-home work, on travel demand by mode, car ownership, and car vehicle miles of travel. This type of model system has the potential of replacing some existing demand forecasting models.

The objective of this paper is to examine reporting errors in panel data obtained from multi-day travel diaries. A distinction is made between within and between wave biases. The former leads to an increase in under-reporting associated with the number of days the diary is kept. The latter is related to the number of waves respondents have been participating, so-called panel experience. These biases imply that observed mobility changes between waves are partly due to reporting errors: without controlling for them, changes in mobility can not be inferred from the data. An important cause of these measurement errors is the increase in the number of days on which no trips at all were reported. In addition, shorter trips and less complex chains are more susceptible to underreporting. The methodology used in this paper provides a means of dealing with these problems. Attrition is taken into account by a rather simple measure. The paper concludes with a number of suggestions for sample and survey design.