A new approach to evaluating on-road public transport priority projects: balancing the demand for limited road-space

Despite widespread growth in on-road public transport priority schemes, road management authorities have few tools to evaluate the impacts of these schemes on all road users. This paper describes a methodology developed in Melbourne, Australia to assist the road management authority, VicRoads, evaluate trade-offs in the use of its limited road-space for new bus and tram priority projects. The approach employs traffic micro-simulation modelling to assess road-space re-allocation impacts, travel behaviour modelling to assess changes in travel patterns and a social cost benefit framework to evaluate impacts. The evaluation considers a comprehensive range of impacts including the environmental benefits of improved public transport services. Impacts on public transport reliability improvements are also considered. Although improved bus and tram reliability is a major rationale for traffic priority its use in previous evaluations is rare. The paper critiques previous approaches, describes the proposed method and explores some of the results found in its application. A major finding is that despite a more comprehensive approach to measuring the benefits of bus and tram priority, road-space reallocation is difficult to economically justify in road networks where public transport usage is low and car usage high. Strategies involving the balanced deployment of bus and tram priority measures where the allocation of time and space to PT minimises negative traffic impacts is shown to improve the overall management of road-space. A discussion of the approach is also provided including suggestions for further methodology development.

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

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.

Modeling children’s school travel mode and parental escort decisions

Understanding of the activity-travel patterns of children is becoming increasingly important to various policy makers. Further, there is also a growing recognition that intra-household interactions need to be explicitly accommodated in travel models for realistic forecasts and policy evaluation. In the light of these issues, this paper contributes towards an overall understanding of the school-travel behavior of children and the related interdependencies among the travel patterns of parents and children. An econometric model is formulated to simultaneously determine the choice of mode and the escorting person for children’s travel to and from school. The 2000 San Francisco Bay Area Travel Survey (BATS) data are used in the model estimation process. Empirical results indicate that the characteristics of child like age, gender, and ethnicity, and employment and work flexibility characteristics of the parents have strong impacts on the mode choice decisions. In addition, the impacts of some of these attributes on the choice of mode to school are different from the corresponding impacts on the choice of mode from school. The distance between home and school is found to strongly and negatively impact the choice of walking to and from school, with the impact being stronger for walking to school. Several land-use and built-environment variables were explored, but were found not to be statistically significant predictors.

Family changes and public transport use 1984–1987

Panel data are used to investigate what happens when people have some important transitions or changes in their life-cycle, employment status, income, or car ownership. Only three years data are available, and the pace of transition is slow: as a result relevant sample sizes are rather small. The analysis is carried out in two stages: results are given from a two year period, and these are used to generate hypotheses checked with data from the third year.It is found that in many cases the effects on mobility are different from what would be expected from cross-section data. In particular:

Grouping travelers on the basis of their different car and transit levels of use

Market segmentation studies in travel behavior research are ordinarily based on socioeconomic characteristics and personality traits. This study explores the usefulness of a different approach, where the actual overall mobility levels across different ground transportation modes, along with desired changes in the use of cars and transit, are used as clustering variables. Using a given mode can in fact influence the personal representation of that mode, which in turn has been proven to be a key element in transport behaviours. We form such multimodality-based clusters from two field studies, one involving employees of the French transportation research institute INRETS and the other a representative sample of residents of the US San Francisco Bay Area. We find that strong users of a given mode would like to bring more balance to their “modal consumptions” by decreasing the use of this mode more than the average, and increasing the use of the alternative mode. However, concerning ground transport travel budgets, the desire to travel more (or less) overall seems less strongly related to the composition of the modal balance. The US dataset shows also a greater latent demand for travel than the French one. Socioeconomic characteristics of the clusters could not explain the patterns that were found, confirming the importance of taking into account multimodality issues in travel behavior research. Some policy implications from these findings are finally reported.

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.

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.

A case for measuring individuals’ access to private-vehicle travel as a matter of degrees: lessons from focus groups with Mexican immigrants in California

In auto-oriented communities, access to an automobile is essential for good mobility, but not everyone owns a car or is able to drive. Little is known about how individuals in these circumstances might still use vehicles for transportation. To provide insight on the nature of vehicle use by those with potentially limited vehicle access, we present qualitative findings from focus groups with recent Mexican immigrants living in California, half of whom owned no cars. Our results demonstrate varying degrees of participants’ access to vehicle travel not always corresponding to auto ownership, with extensive sharing of cars, borrowing of cars, and getting rides. We describe the different dimensions of vehicle access that participants experienced and identify specific factors that seemed to influence their access levels. We discuss the implications of our findings for transportation policy and future research.

Regulation of the informal transport sector in Rio de Janeiro,Brazil: welfare impacts and policy analysis

In Brazil, the explosion of informal transport activity during the past decade has had profound effects on formal public transport systems and is a source of great controversy in the urban transportation sector. A variety of policies have been proposed to manage the growth of the sector. This study seeks to understand how proposed policies will impact the users of these systems. A corridor in Rio de Janeiro with substantial informal activity was used as a case study. Measures of welfare changes in a discrete choice framework were used to estimate proposed policies’ impacts on users. Eleven candidate policies were evaluated, ranging from the eradication of the informal modes and investment in formal modes, to the legalization of the informal modes. Benefits were compared with costs and the distribution of benefits across income classes was explored. Net benefits from some policies were found to be substantial. Legalizing the informal sector was found to benefit users slightly but further investments in the sector are probably inefficient. Users benefited most from improvements in formal mass transit modes, at roughly 100–200 dollars per commuter per year. Finally, policies to foster a competitive environment for the delivery of both informal and formal services were shown to benefit users about 100 dollars per commuter per year. Together, the regulation of the informal sector and investments in the formal sector serve to reinforce the movement towards competitive concessions for services and help reduce the impacts of cartelization and costly in-road competition.

An exploratory analysis of joint-activity participation characteristics using the American time use survey

This paper presents a detailed exploratory analysis of joint activity participation characteristics using the American Time Use Survey (ATUS). As a very large nationwide survey that explicitly elicited information on both household and non-household companions for each activity episode, the ATUS is ideally suited for this analysis. Several intuitive and interesting results are obtained. Joint episodes are found to be of longer durations, significantly likely to take place at the residence of other people, and often confined to certain time periods of the weekday. In addition, important differences in these characteristics are also observed based on activity purpose, companion type, and the day of the week. These findings are intended to provide the basis for the justification of detailed collection of joint activity–travel participation information in household activity–travel surveys, and also as a stimulant for further empirical analysis and modeling of joint activity participation behavior.

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.

Just pricing: the distributional effects of congestion pricing and sales taxes

Those who oppose tolls and other forms of road pricing argue that low-income, urban residents will suffer if they must pay to use congested freeways. This contention, however, fails to consider (1) how much low-income residents already pay for transportation in taxes and fees, or (2) how much residents would pay for highway infrastructure under an alternative revenue-generating scheme, such as a sales tax. This paper compares the cost burden of a value-priced road, State Route 91 (SR91) in Orange County, California with the cost burden under Orange County’s local option transportation sales tax, Measure M. We find that although the sales tax spreads the costs of transportation facilities across a large number of people inside and outside Orange County, it redistributes about $3 million (USD) in revenues from less affluent residents to those with higher incomes. The entire Measure M program redistributes an estimated $26 million from low-income residents to the more affluent. Low-income drivers as individuals save substantially if they do not have to pay tolls, but as a group low-income residents, on average, pay more out-of-pocket with sales taxes.

Correlations between industrial demands (direct and total) for communications and transportation in the U.S. economy 1947–1997

Using input–output (I–O) accounts provided by the U.S. Department of Commerce, this study investigates the aggregate relationships between the transportation and communications inputs demanded (directly and in total) by all industries in the U.S., and compares the results across time. We analyzed five pairs of Spearman correlations of transportation and communications demands (utilities, manufacturing, and overall) using the direct and total coefficient tables from the ten benchmark input–output years spanning 1947 to 1997. To correctly represent the overall economy-wide relationship, each industry (direct table) or commodity (total table) in the correlation was weighted proportionately to the monetary value of its contribution to the U.S. economy. In the analysis using direct I–O coefficients, we found a pattern of predominant complementarity between transportation and communications manufacturing, and substitution between transportation and communications utilities. There are intriguing indications, however, of a shift from substitution to complementarity in the latter case, beginning around 1987. In the analysis using total I–O coefficients, we found a pattern of complementarity for all years between transportation and communications manufacturing, and a pattern changing from substitution to complementarity for the remaining four pairs (transportation manufacturing and communications utilities; transportation utilities and communications manufacturing; the utilities pair; and the overall pair). Thus, from the industrial perspective (which constitutes a sizable proportion of the total demand for communications and transportation), it is not realistic in modern times to expect telecommunications to substitute for travel. Nevertheless, further research is needed into the specific causes of the observed shift from substitution to complementarity, and current trends should continue to be monitored for any changes.

The transition to electric bikes in China: history and key reasons for rapid growth

Annual electric bike (e-bike) sales in China grew from 40,000 in 1998 to 10 million in 2005. This rapid transition from human-powered bicycles, buses and gasoline-powered scooters to an all-electric vehicle/fuel technology system is special in the evolution of transportation technology and, thus far, unique to China. We examine how and why e-bikes developed so quickly in China with particular focus on the key technical, economic, and political factors involved. This case study provides important insights to policy makers in China and abroad on how timely regulatory policy can change the purchase choice of millions and create a new mode of transportation. These lessons are especially important to China as it embarks on a large-scale transition to personal vehicles, but also to other countries seeking more sustainable forms of transportation.

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.

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.

Traveled distance,stock and fuel efficiency of private vehicles in Canada: price elasticities and rebound effect

This paper presents estimates of the rebound effect and other elasticities for the Canadian light-duty vehicle fleet using panel data at the provincial level from 1990 to 2004. We estimate a simultaneous three-equation model of aggregate demand for vehicle kilometers traveled, vehicle stock and fuel efficiency. Price and income elasticities obtained are broadly consistent with those reported in the literature. Among other results, an increase in the fuel price of 10% would reduce driving by ~2% in the long term and by 1% the average fuel consumption rate. Estimates of the short- and long-term rebound effects are ~8 and 20%, respectively. We also find that an increase in the gross domestic product per capita of 10% would cause an increase in driving distance of 2–3% and an increase of up to 4% in vehicle stock per adult. In terms of policy implications, our results suggest that: (1) the effectiveness of new fuel efficiency standards will be somewhat mitigated by the rebound effect and (2) fuel price increases have limited impacts on gasoline demand.

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.

Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models

The rapid and continuing changes in travel and mobility needs in India over the last decade necessitates the development and use of dynamic models for travel demand forecasting rather than cross-sectional models. In this context, this paper investigates mode choice dynamics among workers in Chennai city, India over a period of five years (1999–2004). Dynamics in mode choice is captured at four levels: exogenous variable change, state-dependence, changes in users’ sensitivity to attributes, and unobserved error terms. The results show that the dynamic models provide a substantial improvement (of over 500 log-likelihood points and ρ² increases from 44% to 68%) over the cross-sectional model. The performance was compared using two illustrative policy scenarios with important methodological and practical implications. The results indicate that cross-sectional models tend to provide inflated estimates of potential improvement measures. Improving the Level of Service (LOS) alone will not produce the anticipated benefits to transit agencies, as it fails to overcome the persistent inertia captured in the state-dependence factors. The results and models have important applications in the context of growing motorization and congestion management in developing countries.