Cordon tolls and competition between cities with symmetric and asymmetric interactions

The aim of this paper is to model the impacts of competition between cities on both the optimal welfare generating tolls and upon longer-term decisions such as business and residential location choices. The research uses a dynamic land use transport interaction model of two neighbouring cities and analyses the impacts by setting up a game between the two cities to maximise the welfare of their own residents. The work builds on our earlier research which studied competition in a small network using a static equilibrium approach for private car traffic without accounting for the land use responses to the change in accessibility. This paper extends the earlier work by setting up a dynamic model which includes active modes of travel and the more usual car and public transport in a realistic twin city setting and assesses the longer term relocation responses. This paper firstly sets out the competition between two hypothetical identical cities i.e. the symmetric case; and then sets out the real world asymmetric case in which the cities are of different size representative of Leeds and Bradford in the UK but equally applicable elsewhere too. We found that the level of interaction between the two cities is a key determinant to the optimal tolls and welfare gains. Our findings show that the competition between cities could lead to a Nash Trap at which both cities are worse off in terms of welfare gains. On the other hand, we found that cities, if regulated, would gain in terms of welfare and yet charge only half the toll compared with tolls under competition. We then show that the effect of competition increases with increased interaction between cities. In terms of residential location, cities with higher charges benefit from an increase in residents, though as with other studies, the relative change in population in response to cordon charging is small. The policy implications are threefold—(i) while there is an incentive to cooperate at local authority level, this is not achieved due to competition; (ii) where cities compete they may fall into a Nash Trap where both cities will be worse off compared to the regulated solution; and (iii) regulation is recommended when there is a strong interaction between the cities but that the benefits of regulation decrease as interaction between cities decreases and the impact of competition is lessened.     

Methodology for the qualitative evaluation of pedestrian crossings at road junctions with traffic lights

The aim of this work is to test the application of a method for making a qualitative evaluation of pedestrian crossings, based on the methodology of Khisty (Transportation Research Record 1438:45–50, 1994). The study identifies the Performance Measures (Comfort, Safety, System Continuity), with their respective attributes (waiting time, space available while waiting to cross, number of pedestrians, one-way or two-way street, state of the road surface, road width, vehicle speed, visibility, lighting conditions, guardrails, absence of obstacles in vicinity, state of sidewalks, lowered kerb, pedestrian signals, central island), which may be utilized in the evaluation. The first step was to ascertain the relative importance, from the point of view of the pedestrian, of the Performance Measures employed. Then the level-of-service (LOS), as perceived by the users, was determined for each of the pedestrian crossings in the survey, on the basis of the users’ level of satisfaction with each attribute. Khisty’s methodology makes it possible to relate the overall level of satisfaction with a qualitative LOS for the pedestrian facility under analysis. The chosen methodology was adapted to the Brazilian context, in a case study carried out in the city of São Paulo (Brazil), in collaboration with the local Traffic Engineering Corporation (Companhia de Engenharia de Tráfego, CET-SP). To this end, four pedestrian crossings at road junctions with traffic lights were analysed. The qualitative LOS obtained were compared to the quantitative LOS, calculated according to the Highway Capacity Manual (TRB 2000).     

Predictors of car use habit strength in an urban Norwegian public

Car use in urban areas carries well known risks for negative effects on urban quality of life, the environment and land use. Previous psychological work has mainly studied the habit of using a car as a predictor variable, whereas only a few studies have focused on psychological factors that may underlie and shape the habit. The aim of the present study is to examine demographic characteristics, spatial variables and social–psychological factors associated with car habit use strength. The results are based on a survey conducted in a random urban population-based sample with car access recruited from the Norwegian population registry (n = 878). Car use habit strength was stronger among male than female respondents and tended to be weak with a lower income. Multivariate logistic regression showed that reporting pro-environmental attitudes and personal norms predicted a weak car use habit strength, while reporting strong priority of flexibility and a long distance from home to work predicted a strong car use habit strength. Car use habit is influenced by multiple demographic, spatial and social–psychological factors, which should be carefully addressed in interventions aimed at reducing the likelihood of developing a strong car habit strength.     

Differences in probability weighting for individual travelers: a managed lane choice application

Using a 2012 stated preference survey based on a traveler’s most recent actual trip, this study predicts traveler choices between general purpose lanes and managed lanes for a freeway in Houston, Texas. The choice model incorporates probability weighting for risky travel times. The results indicate significant improvement in predicative power over a model that excludes weighting, confirming non-linearity in the probability weighting function. The maximum value of time (VOT) measures calculated in this study are lower than estimated in many previous route choice studies. This highlights the importance of incorporating individual weights for travel risks. Travelers’ underweighting of travel time risks would help explain the lower VOTs found in our study because respondents consider route choice decision-making as a gamble, but assign their own probabilities of occurrence to arriving at their destination on time, late, or early. We find that traveler groups are heterogeneous and the different weights developed for different groups of travelers can be used to better understand their probabilities. Segmentation analysis indicates that Age may serve to proxy the effects of more experience over time, or changing driving abilities, or changes in one’s sense of optimism or pessimism at different ages. Gender and Income also play a role in how the objective probabilities presented to respondents were translated into subjective probabilities.     

Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence

Travel behaviour analysis has recently witnessed a rapidly growing interest in regret-based models of choice behaviour. Two different model specifications have been introduced in the transportation literature. Chorus et al. (Transportation Research B 42: 1–18, 2008a; in: Proceedings 87th Annual Meeting of the Transportation Research Board, Washington DC, 2008b) specified regret as a (non) linear function of the difference between the best-foregone choice alternative and the chosen alternative. Later, as an approximation to the original specification, Chorus (2010) suggested a logarithm function of utility differences between all choice alternatives, mainly for ease of estimation. This paper makes two contributions to this literature. First, formal analyses are conducted to identify the parameter space where the logarithmic specification becomes theoretically inferior to the original specification. Second, an empirical stated choice study on the choice of shopping centre is conducted to empirically test which specification best describes stated choices. Results suggest that for the collected data the original specification outperforms the new specification. Implications of this finding for the application of regret-based choice models in travel behaviour analysis are discussed.     

Variation of households<Emphasis Type="Bold">’</Emphasis> car ownership across time: application of a panel data model

For economic and environmental policy formulation and with the effort of creating less car dependent societies, it is important to study the changing characteristics of car ownership in a household through time as well as factors responsible of these variations. There is a vast body of literature on empirical studies of car ownership and use. These studies have investigated the socio-economic background of the decision maker, the built environment and the perception associated with owning a car as determinant factors of car ownership and use. In most cases, these analyses have been carried out using cross-sectional data sets. However, the analysis of factors determining changes in travel behavior of an individual or household requires information on their behavior over time (longitudinal data set). In this study, the German Mobility Panel (1996–2006) is used to examine variation of car ownership through time and across households. The panel data modeling results showed that there are variations of car ownership between households whereas changes in car ownership of a given household over time (within household variations) are insignificant. The influence of other factors such as the households’ socio-economic background, the availability of public transportation and shopping/leisure facilities, perception on parking difficulties and satisfaction with existing public transportation services on the car owning characteristics of households is also presented and discussed in this paper.

User preferences regarding autonomous vehicles

This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:

• (1)Continue to commute using a regular car that you have in your possession.
• (2)Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
• (3)Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.

A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.     

A method to evaluate equitable accessibility: combining ethical theories and accessibility-based approaches

In this paper, we present the case that traditional transport appraisal methods do not sufficiently capture the social dimensions of mobility and accessibility. However, understanding this is highly relevant for policymakers to understand the impacts of their transport decisions. These dimensions include the distribution of mobility and accessibility levels over particular areas or for specific population groups, as well as how this may affect various social outcomes, including their levels of participation, social inclusion and community cohesion. In response, we propose a method to assess the socially relevant accessibility impacts (SRAIs) of policies in some of these key dimensions. The method combines the use of underlying ethics principles, more specifically the theories of egalitarianism and sufficientarianism, in combination with accessibility-based analysis and the Lorenz curve and Gini index. We then demonstrate the method in a case study example. Our suggestion is that policymakers can use these ethical perspectives to determine the equity of their policies decisions and to set minimum standards for local transport delivery. This will help them to become more confident in the development and adoption of new decision frameworks that promote accessibility over mobility and which also disaggregate the costs and benefits of transport policies over particular areas or for specific under-served population groups.     

What encourages people to carpool? An evaluation of factors with meta-analysis

Non-household carpools (where two or more commuters from different residences travel together in the same private vehicle) bring public benefits. To encourage and incentivise it, transport practitioners and researchers must understand its private motivations and deterrents. Existing studies often report conflicting results or non-generalisable findings. Thus, a quantitative systematic review of the literature body is needed. Using meta-analysis, this study synthesised 22 existing empirical studies (representing over 79,000 observations) to produce an integrated review of the carpooling literature. The meta-analysis determined 24 non-household carpooling factors, and their effect sizes. Factors such as number of employees (\(\bar{r} = 0.42\)), partner matching programs (\(\bar{r} = 0.42\)), female (\(\bar{r} = 0.22\)) and fixed work schedule (\(\bar{r} = 0.15\)) were found to have strong effects on carpooling while judgmental factors (such as the motivation to save costs) only exhibited small influence (\(\bar{r} < 0.1\)). Based on the significant effects, the paper discussed prospects for improving carpooling uptake by developing: (i) target demographics, (ii) selling points for marketing, (iii) carpooling partner programs and (iv) multiple employer ‘super-pools’. The results warrant caution due to the small amount of studies synthesised. Transport practitioners might plan carpooling policies based on the findings; and transportation researchers might use the list of factors to model carpooling behaviour.     

Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model

The causality issue has become one of the key questions in the debate over the relationships between the built environment and travel behavior. Although previous studies have tested statistical and/or practical significance of the built environment on travel behavior, few have quantified the relative roles of the built environment and residential self-selection in influencing travel behavior. Using 1,479 residents living in four traditional and four suburban neighborhoods in Northern California, this study explores the causal effect of neighborhood type on driving behavior and its relative contribution to the total influence of neighborhood type. Specifically, this study applied Heckman’s sample selection model to separate the effect of the built environment itself and the effect of self-selection. The results showed that, on average, the effect of neighborhood type itself on driving distance was 25.8 miles per week, which accounted for more than three quarters of the total influence of neighborhood type and 16% of individuals’ overall vehicle miles driven. These results suggest that the effect of the built environment on driving behavior outweighs that of self-selection. This paper also discussed the advantages and weaknesses of applying the Heckman’s model to address the self-selection issue.

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.

Estimating the long-term effects of different passenger car technologies on energy/fuel consumption and emissions of greenhouse gases in Europe

Abstract

A stated preference (SP) experiment of car ownership was conducted in Mumbai Metropolitan Region (MMR) of Maharashtra in India. A full factorial experiment was designed to considering various attributes such as travel time, travel cost, projected household income, car loan payment and servicing cost. Data on 357 individuals were collected which resulted in 3213 observations for the calibration of the work trip and recreational trip car ownership models. The car ownership alternatives considered 0, 1 and 2 cars. A multinomial logit framework was used to develop the car ownership model taking the household as a decision unit. The specification and results of the SP car ownership model are discussed. The observed and predicted values matched reasonably when the validity of the SP car ownership model was tested against revealed preference (RP) data. The car ownership models developed in this study exhibit a satisfactory goodness of fit. It is concluded that the SP modelling approach can be successfully used for modelling car ownership decisions of households in developing countries.     

Comparative analysis of household car,motorcycle and bicycle ownership between Osaka metropolitan area,Japan and Kuala Lumpur,Malaysia

The interactions among different types of vehicle ownership including car, motorcycle and bicycle are examined by developing simultaneous vehicle ownership models in this study. Large scale person trip survey data for Osaka metropolitan area, Japan and Kuala Lumpur, Malaysia are used for empirical analysis. The results suggest that population density at residential area significantly and negatively affects car ownership for both areas, and that the effects are larger for Osaka metropolitan area than for Kuala Lumpur. Also, bicycle ownership becomes higher at higher population density area for Osaka area, while higher at lower population density area for Kuala Lumpur, which represents the different usage patterns of bicycle between the two areas.

Modular route bus design – A method of meeting transport operation and vehicle manufacturing requirements

This research examines the problem of route bus specification and vehicle manufacturability. In order for bus operators to provide transport services, a range of vehicle configurations must be available from bus manufacturers, generating variety which has a negative impact on the manufacturing process. Larger part inventories, uncontrolled labour tasks and more troublesome maintenance are known impacts of this variety. This research identifies the functional necessities in route bus interior design and reduces the problems in bus manufacture and operation caused by specification diversity by proposing a modularised system of bus design. In particular, it makes recommendations as to how bus configuration should be carried out, ensuring an optimum mix of operational and manufacturing needs:

• 1.Determine user needs before the bus specification process.
• 2.Designs to be developed by the manufacturer in response to user needs.
• 3.This design should be standardised where possible, as suggested by the user needs.
• 4.Where user needs dictate product variations, apply a mass customisation approach to accommodate these needs.

The recommendations are communicated in design proposals for a modular bus interior, demonstrated by four cases designed to meet the present status quo of bus interior design and predictions for the future of the field.     

The ambivalence of ridesharing

Ridesharing is quite a popular topic of discussion among transport authority personnel. It is perceived to be a viable alternative to classical modes of transportation, and receives a great deal of political support from transport planners. However, not much objective information is available on ridesharing behaviors. We use travel survey data to study the evolution of the ridesharing market in an urban area. Our study is based on data from four large-scale OD surveys conducted in the Greater Montreal Area (1987, 1993, 1998 and 2003). In the latest survey conducted in Montreal, car passengers were asked to identify the driver who gave them the opportunity to travel in this way. Their answers were classified according to the type of driver; for instance, a member of their household, a neighbor or a co-worker. We use this information to calibrate a model matching car passengers and car drivers belonging to the same household. This will be referred to as IHHR (intra-household ridesharing). Preliminary results reveal that approximately 70% of all trips made by car passengers are the result of IHHR. Furthermore, around 15% of those trips are questionable, in that they were exclusively generated for another individual’s purposes, consequently generating an additional trip for the journey back home. Moreover, this percentage increased over time. Objective data regarding ridesharing and its evolution in an urban area will undoubtedly help decision makers gain a clearer profile of this means of travel and help to realign attitudes on the issue.

Household-level commuting mode choices,car allocation and car ownership level choices of two-worker households: the case of the city of Toronto

The paper presents a comprehensive investigation on household level commuting mode, car allocation and car ownership level choices of two-worker households in the City of Toronto. A joint econometric model and a household travel survey dataset are used for empirical investigations. Empirical models reveal that significant substitution patterns exist between auto driving and all other mode choices in two-worker households. It is revealed that, female commuters do not prefer auto driving, but in case of a one car (and two commuters with driving licenses) household, a female commuter gets more preference for auto driving option than the male commuter. Reverse commuting (commuting in opposite direction of home to central business district) plays a critical role on household level car allocation choices and in defining the stability of commuting behaviour of two-worker households. Two worker households in higher income zones and with longer commuting distances tend to have higher car ownership levels than others. However, higher transit accessibility to jobs reduces household car ownership levels. The study reveals that both increasing two worker households and reverse commuting would increase dependency on private car for commuting.     

A tour-based model of travel mode choice

This paper presents a new tour-based mode choice model. The model is agent-based: both households and individuals are modelled within an object-oriented, microsimulation framework. The model is household-based in that inter-personal household constraints on vehicle usage are modelled, and the auto passenger mode is modelled as a joint decision between the driver and the passenger(s) to ride-share. Decisions are modelled using a random utility framework. Utility signals are used to communicate preferences among the agents and to make trade-offs among competing demands. Each person is assumed to choose the best combination of modes available to execute each tour, subject to auto availability constraints that are determined at the household level. The households allocations of resources (i.e., cars to drivers and drivers to ride-sharing passengers) are based on maximizing overall household utility, subject to current household resource levels. The model is activity-based: it is designed for integration within a household-based activity scheduling microsimulator. The model is both chain-based and trip-based. It is trip-based in that the ultimate output of the model is a chosen, feasible travel mode for each trip in the simulation. These trip modes are, however, determined through a chain-based analysis. A key organizing principle in the model is that if a car is to be used on a tour, it must be used for the entire chain, since the car must be returned home at the end of the tour. No such constraint, however, exists with respect to other modes such as walk and transit. The paper presents the full conceptual model and estimation results for an initial empirical prototype. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression.     

Car availability change in England and Wales 1971–1981

The aggregate dynamics of car ownership have overshadowed the dynamics of car ownership and availability at the personal and household level. These dynamics have only recently been investigated in more depth. This paper contributes to this work by probing a special data source, theLongitudinal Study (LS) produced by the Office of Population Censuses and Surveys, for the changes in household car availability.The paper describes the LS and explains its special format, as a Census-based ~1% sample of the population of England and Wales.The analysis focuses on the car availability dynamics of a number of groups defined by changes in their life cycle position. Special attention is given to those households where the LS member remains a dependent child throughout the study period.The results show that all studied household types increase and decrease their car holdings, but that there are patterns in this process, which vary from group to group. In particular, the size of the previous car fleet has a different influence on the current fleet size from life cycle group to life cycle group.The paper argues in its conclusion to incorporate these differentials into the further work on car ownership and car ownership change.The work reported here was performed, while the author was a staff member of the Centre for Transport Studies, Imperial College, London.     

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.

Using census aggregates to proxy for household characteristics: an application to vehicle ownership

Traditionally, researchers studying transportation choice have used data either acquired from household surveys or broad, region-wide aggregates. At the disaggregate level, researchers usually do not have access to important variables or observations. This study investigates the potential usefulness of a proxy approach to modeling discrete choice vehicle ownership: substituting narrow area-based aggregate proxies for missing micro-level explanatory variables by accessing large, publicly maintained datasets. We use data from the 2000 Bay Area Travel Survey (BATS) and the contemporaneous U.S. Census file to compare three models of vehicle ownership, drawing area-wide proxies from increasing levels of aggregation. The models with proxies are compared with a parallel model that uses only survey data. The results indicate that the proxy models are preferred in terms of model selection criteria, and predict vehicle ownership as well or better than the survey model. Parameter values produced by the proxy method effectively approximate those returned by household survey models in terms of coefficient sign and significance, particularly when the aggregate variables are representative of their household-level counterparts. The proxy model with the narrowest level of aggregation achieved the best fit, coefficient precision, and percentage of correct prediction.