What is responsible for the response lag of a significant change in discretionary time use: the built environment,family and social obligations,temporal constraints,or a psychological delay factor?

In this paper, we used the 10-wave Puget Sound Panel Dataset to investigate the response lag of a significant change in discretionary time use. In particular, we want to quantify the relative magnitude of the following factors: the built environment, family and social obligations, temporal constraints, or a psychological delay factor (people delay a behavioral change until the next life shock). To answer this question, we developed a survival model to treat (1) left-censoring, (2) partial observation, and (3) multi-type exits. The results suggest that family and social obligations, as well as temporal constraints, appear to play a more important role than the built environment. Support for the psychological delay factor is not evident. We also found that the probability of having a significant change in discretionary time use is negatively related to time progression, supporting the human adaptivity hypothesis.

Residential location,vehicle ownership and travel in Asia: a comparative analysis of Kei-Han-Shin and Kuala Lumpur metropolitan areas

The study examines the relationships between residential location, vehicle ownership and mobility in two metropolitan areas of Asia, Kei-Han-Shin area of Japan and Kuala Lumpur area of Malaysia. It shows that, behind apparent similarities of household auto ownership and travel time expenditure per household member, there are many causal relationships that are distinct between the areas. The similarities and differences between the two areas point to the conjecture that the evolution of a metropolitan area may be unique and path dependent, being heavily influenced by the history and culture of the locale, spatial and geographical constraints, and historical progression in infrastructure development.

Modeling residential sorting effects to understand the impact of the built environment on commute mode choice

This paper presents an examination of the significance of residential sorting or self selection effects in understanding the impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found that built environment attributes can indeed significantly impact commute mode choice behavior. The paper concludes with a discussion of the implications of the model findings for policy planning.

Information impact on quality of multimodal travel choices: conceptualizations and empirical analyses

This paper investigates the impact of a variety of travel information types on the quality of travel choices. Choice quality is measured by comparing observed choices made under conditions of incomplete knowledge with predicted choice probabilities under complete knowledge. Furthermore, the potential impact of travel information is considered along multiple attribute-dimensions of alternatives, rather than in terms of travel time reductions only. Data is obtained from a choice experiment in a multimodal travel simulator in combination with a web-based mode-choice experiment. A Structural Equation Model is estimated to test a series of hypothesized direct and indirect relations between a traveler’s knowledge levels, information acquisition behavior and the resulting travel-choice quality. The estimation results support the hypothesized relations, which provides evidence of validity and applicability of the developed measure of travel-choice quality. Furthermore, found relations in general provide some careful support for the often expected impact of information on the quality of travel choices. The effects are largest for information services that generate previously unknown alternatives, and lowest for services that provide warnings in case of high travel times only.

Urban form,travel time,and cost relationships with tour complexity and mode choice

The primary purpose of this study was to investigate how relative associations between travel time, costs, and land use patterns where people live and work impact modal choice and trip chaining patterns in the Central Puget Sound (Seattle) region. By using a tour-based modeling framework and highly detailed land use and travel data, this study attempts to add detail on the specific land use changes necessary to address different types of travel, and to develop a comparative framework by which the relative impact of travel time and urban form changes can be assessed. A discrete choice modeling framework adjusted for demographic factors and assessed the relative effect of travel time, costs, and urban form on mode choice and trip chaining characteristics for the three tour types. The tour based modeling approach increased the ability to understand the relative contribution of urban form, time, and costs in explaining mode choice and tour complexity for home and work related travel. Urban form at residential and employment locations, and travel time and cost were significant predictors of travel choice. Travel time was the strongest predictor of mode choice while urban form the strongest predictor of the number of stops within a tour. Results show that reductions in highway travel time are associated with less transit use and walking. Land use patterns where respondents work predicted mode choice for mid day and journey to work travel.

The impact of urban form on automobile travel: disentangling causation from correlation

A longstanding question within the field of transportation demand management is the strength of the relationship between urban form and mobility behavior. Although several studies have identified a strong correlation between these variables, there is as yet scant evidence to support policy interventions that target land use as a means of influencing travel. To the contrary, some of the more recent research has cast skepticism on the proposition that the relationship is causative, recognizing the possibility that households endogenously self-select themselves into communities that support their preferences for particular transportation modes. Focusing on individual automobile travel, the present study seeks to contribute to this line of inquiry by estimating econometric models on a panel of travel-diary data collected in Germany between 1996 and 2003. Specifically, we employ the two-part model (2PM)—a procedure involving probit and OLS estimators—to assess the determinants of the discrete decision to use the car and the continuous decision of distance traveled. Beyond modeling variables that capture the urban form features that are commonly suggested to influence mobility behavior, including mixed use and public transit, this study employs instrumental variables to control for potential endogeneity emerging from the simultaneity of residential and mode choices. Unlike much of the work to date, our results suggest that urban form has a causative impact on car use, a finding that is robust to alternative econometric specifications.

A week in the life of baby boomers: how do they see the spatial–temporal organization of their activities and travel?

In this paper, we take an initial look at the spatial and temporal flexibility in the activity patterns of the so-called “baby-boomer” cohort (born 1947–1966) in comparison with younger and older adults. Using a unique longitudinal survey carried in Quebec City from 2002 to 2005, we explore activity patterns and trip rates over a seven-day observation period during the first wave, and take a first look at some aspects of their evolution over two subsequent waves at about one-year intervals. We model the propensity to undertake activities within selected conventional non-work classifications such as “shopping” and “leisure”, and also according to respondents’ own perceptions of the spatial and temporal flexibility of each out-of-home activity that they had executed. While we cannot strictly separate cohort effects from age-related effects, after controlling for gender and household structure, we infer that age and related lifestyle effects dominate in explaining these propensities. However, the boomers were the only age stratum to increase their total out-of-home activity participation over the course of the panel, an intriguing starting point for the future study of this cohort.

Understanding the relationships between private automobile availability, overall physical activity, and travel behavior in adults

In most developed countries motorized transportation is the dominant form of travel for long and short journeys. Transport-related physical activity (TPA), however, is advocated as an appropriate transport mode for traveling short distances. The purpose of this study is to explore the associations between private automobile availability, overall physical activity levels, and TPA engagement in the adult population. A population-representative telephone survey assessed socio-demographics, private automobile availability, overall physical activity levels, and travel to place of work/study and the convenience shop with an adult sample (n = 2,000) residing in North Shore City, Auckland, New Zealand in April 2005. The majority of respondents reported unrestricted (80%) or frequent (12%) private automobile availability. After controlling for covariates, binary logistic regression analyses revealed those with no private automobile available were less likely to be classified as sufficiently active for health benefits when compared to respondents with unrestricted private automobile availability. However, this finding was based on a small minority (4%). Also, those reporting no private automobile availability were more likely to walk or cycle to place of employment and the convenience shop when compared to those with unrestricted private automobile availability. Similar to other self-report travel and physical activity survey tools, the questionnaire used potentially did not adequately capture TPA engagement. Future TPA research needs to incorporate objective measures to address this issue.

An analysis on long term emission benefits of a government vehicle fleet replacement plan in northern illinois

There have been a number of studies of the effectiveness of vehicle scrappage programs, which offer incentives to accelerated scrappage of older vehicles often thought to be high emitters. These programs are voluntary and aimed at replacement of household vehicles. In contrast, there is a gap in knowledge related to the emissions benefits of government fleet replacement (retirement) programs. In this study, the efficacy of a fleet replacement program for a local government agency in Northern Illinois, the Forest Preserve of DuPage County (FPDC), is examined using a probabilistic vehicle survival model that accounts for time-varying covariates such as vehicle age and gasoline price. The vehicle lifetime operating emissions are calculated based on the estimated vehicle survival probabilities from the survival model and compared with those derived using the EPA default fleet used in MOBILE6 and the fleet represented by the Oak Ridge National Laboratory (ORNL) survival curve. The results suggest that while there may be short term emission benefits of the FPDC fleet replacement plan, the long-term emission benefits are highly sensitive to economic factors (e.g., future gasoline price) and exhibit a decreasing trend. This indicates that an adaptive multi-stage replacement strategy as opposed to a fixed one is preferable to achieve optimal cost effectiveness.

The impact of high-speed technology on railway demand

This paper estimates a passenger railway demand function to analyse effects arising from the introduction and use of high-speed technologies. The paper reports estimates of demand elasticities with respect to price, income, quality of service and a range of exogenous characteristics. The results show that travel time savings from conventional high-speed technology have a larger impact on passenger demand than tilting train technology. The introduction of conventional high-speed technology is associated with an 8% increase in passenger railway demand. Increasing the use of either type of high-speed technology appears to induce small positive effects on demand beyond those obtained from usual traffic density increases on non-high-speed existing technology.

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

The influence of built environment to the trends in commuting journeys in the Netherlands

In this paper we describe commuting trends in the Netherlands in the past decade and examine the influence of urban form and travel accessibility on commuting journeys over time on the basis of data from the Dutch National Travel Survey. Exploratory analysis is performed to identify changes in commuting participation, departure time, commuting time, commuting distance and the modal split. Regression analysis and choice models are used to examine the influence of the built environment on commuting parameters over time. The results indicate that urban form has consistently influenced the parameters of commuting journey in the Netherlands in the last 10 years. However, the trend of the influence is unique for each commuting model. Some influences have become less significant in the last decade and some have become stronger.

What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics

In recent years, there have been studies of the influence of neighborhood or built environment characteristics on residential location choice and household travel behavior. Interestingly, there is no uniform definition of neighborhood in the literature and the definition is often vague. This paper presents an alternative way of defining neighborhood and neighborhood type, which involves innovative usage of public data sources. Furthermore, the paper investigates the interaction between neighborhood environment and household travel in the US. A neighborhood here is spatially identical to a census tract. A neighborhood type identifies a group of neighborhoods with similar neighborhood socio-economic, demographic, and land use characteristics. This is accomplished by performing log-likelihood clustering on the Census Transportation Planning Package (CTPP) 2000 data. Five household travel measures, i.e., number of trips per household, mode share, average travel distance and time per trip, and vehicle miles of travel (VMT), are then compared across the resulting 10 neighborhood types, using the 2001 National Household Travel Survey (NHTS) household and trip files. It is found that household life cycle status and residential location are not independent. Transit availability at place of residence tends to increase the transit mode share regardless of household automobile ownership and income level, and job-housing trade-offs are evident when mobility is not of concern. The study also reveals racial preference in residential location and contrasting travel characteristics among ethnic groups.

Children’s mode choice for the school trip: the role of distance and school location in walking to school

Rising levels of childhood obesity in the United States and a 75% decline in the proportion of children walking to school in the past 30 years have focused attention on school travel. This paper uses data from the US Department of Transportation’s 2001 National Household Travel Survey to analyze the factors affecting mode choice for elementary and middle school children. The analysis shows that walk travel time is the most policy-relevant factor affecting the decision to walk to school with an estimated direct elasticity of −0.75. If policymakers want to increase walking rates, these findings suggest that current policies, such as Safe Routes to School, which do not affect the spatial distribution of schools and residences will not be enough to change travel behavior. The final part of the paper uses the mode choice model to test how a land use strategy—community schools—might affect walking to school. The results show that community schools have the potential to increase walking rates but would require large changes from current land use, school, and transportation planning practices.

A detailed analysis of how an urban trail system affects cyclists’ travel

Transportation specialists, urban planners, and public health officials have been steadfast in encouraging active modes of transportation over the past decades. Conventional thinking, however, suggests that providing infrastructure for cycling and walking in the form of off-street trails is critically important. An outstanding question in the literature is how one’s travel is affected by the use of such facilities and specifically, the role of distance to the trail in using such facilities. This research describes a highly detailed analysis of use along an off-street facility in Minneapolis, Minnesota, USA. The core questions addressed in this investigation aim to understand relationships between: (1) the propensity of using the trail based on distance from the trip origin and destination, and (2) how far out of their way trail users travel for the benefit of using the trail and explanatory factors for doing so. The data used in the analysis for this research was collected as a human intercept survey along a section of an off-street facility. The analysis demonstrates that a cogent distance decay pattern exists and that the decay function varies by trip purpose. Furthermore, we find that bicyclists travel, on average, 67% longer in order to include the trail facility on their route. The paper concludes by explaining how the distance decay and shortest path versus taken path analysis can aid in the planning and analysis of new trail systems.

Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects.

Singapore motorisation restraint and its implications on travel behaviour and urban sustainability

The example of Singapore shows that rapid urban and economic growth does not have to bring traffic congestion and pollution. Singapore has chosen to restrain car traffic demand due to its limited land supply. Transport policy based on balanced development of road and transit infrastructure and restraint of traffic has been consistently implemented for the past 30 years. Combined with land use planning, it resulted in a modern transport system, which is free from major congestion and provides users with different travel alternatives. As the economic growth caused a substantial increase in demand for cars, several pricing policies were introduced with the aim of restraining car ownership and usage. Growth of the vehicle population is now controlled and potentially congested roads are subject to road pricing. These measures help to keep the roads free from major congestion, maintain car share of work trips below 25% and keep the transport energy usage low. Although Singapore conditions are in many aspects unique, its travel demand experience can provide useful lessons for other rapidly growing cities in Asia.

The potential demand for a urban freight consolidation centre

This paper reports the results of a stated-preference study aimed at investigating how transport decisions are made by receivers or by transport operators about the potential use of an urban freight consolidation centre in the city of Fano, Italy. Because there are no revealed preference data, a stated-choice methodology is used. The stated-choice experiments present two alternatives—one using a private vehicle subject to various traffic regulations and one using the urban freight consolidation centre with varying cost and efficiency levels. Conventional discrete choice data modelling shows that the potential demand is influenced mainly by the distance of the parking bay from the shop, by access permit cost, by the service cost of the urban freight consolidation centre, and by the delay in delivery time. Simulations are then performed to assess how the potential demand is affected by various incentives and regulations affecting urban goods distribution.

Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

An integrated spatio-temporal GIS toolkit for exploring intra-household interactions

This paper reports on the development of an integrated spatio-temporal GIS toolkit that facilitates the exploration of intra-household interactions. Two tools comprise the toolkit. The first tool, Space-Time Coincidence Analyst, identifies joint activity/travel episodes undertaken by household members. The core of this tool is a set of flexible criteria for classifying episodes as either joint or independent. The second tool, Space-Time Path Visualizer, not only displays space-time paths for household members, but also shows joint episodes undertaken by any two household members together. The toolkit can be applied to any household-based, activity/travel data set so long as required information is specified by the user. To demonstrate its usefulness for research, the toolkit is applied to the TAPS (Toronto Activity Panel Survey) 2002–03 data set. The results suggest that considerable variation exists in the number of joint activity/travel episodes identified using different classification criteria. Specifically, when using restrictive criteria (i.e., same timing, specific activity type/travel mode), only 2,265 joint activity/travel episodes are identified compared to 8,791 when using more flexible criteria. In turn, our results show that certain key attributes for independent and joint activity/travel episodes (i.e., frequency per household, starting time, ending time and duration) also vary under the different classification criteria.