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.

Financing public transport systems in Kuala Lumpur,Malaysia: challenges and prospects

Most major cities across the world today are facing an intractable challenge of financing public transport. In Kuala Lumpur for example, public transport services are somewhat poor in part because of the failure of major operators to secure ample funding. Previous funding programs implemented in the city have failed to produce a replicable model for financing public transport. Due to numerous financial problems and the dismal performance of privately owned transport firms, the State has in the recent past emerged as a key source of funding for the public transport sector in Kuala Lumpur. This article argues that, despite the insuperable challenges, prospects for the future funding of public transport in Kuala Lumpur appears to be good. The article also draws lessons from both Tokyo and Hong Kong. In order to address the funding deficit facing the public transport industry in the city it is crucial that more viable strategies and policies such as value capture and public–private sector partnerships are adopted by the urban authorities.

Utilization patterns of park and ride facilities among Kuala Lumpur commuters

A parking utilization survey was undertaken with the main objective of analyzing and comparing the daily workday utilization patterns of two main park and ride stations within the Kuala Lumpur conurbation. This study also aimed to gauge the level of usage of the park and ride facilities. The findings showed that the overall utilization pattern of the facilities was very high with a utilization rate of between 80 and 95%. The stations, however, recorded a contrasting accumulation pattern. The study further revealed that most of the rail-based suburban park and ride users were long term parkers. The results of this study are comparable to results of similar studies in Seoul, Calgary, Tyne and Wear and others. Since parking availability is an important factor that has influence on the behavior of a park and ride user, more accurate information relating to the supply and demand of the park and ride facility will assist in planning new transport infrastructure.

The use of a hazard-based duration model for imputation of missing personal income data

This paper suggests using a proportional hazard model to predict personal income, for the purpose of imputing missing income data in household travel surveys. The model has a hazard function that comprises two multiplicative components: (1) a non-parametric baseline hazard function that is dependent only on the income level and (2) a function that is dependent only on the other personal attributes of the survey respondents (excluding income). To estimate and validate the model, data is drawn from a travel characteristics survey conducted in Hong Kong in year 2001. The model is found to have a much higher accuracy when compared with a conventional ordered probit model based on the assumption that the logarithm of income is normally distributed.

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

Separation of car-dependent commuters from normal-choice riders in mode-choice analysis

The ever-increasing use of cars is a big problem in metropolitan areas. To manage the traffic stream and alleviate air pollution, most metropolitan governments are attempting to discourage the use of cars. Nevertheless, the results have not been satisfactory. It is well known that normal-choice riders choose their travel mode based on utility, which is determined by mode-specific impedances and individual characteristics. On the other hand, this study focuses on identifying car-dependent commuters who tend to keep driving cars regardless of the circumstances they are confronted with. For this study, psychometric factors characterizing car-dependent commuters were investigated. However, the performance of the mode-choice model was not sufficiently enhanced despite incorporation of the psychometric factors. The performance improved considerably when the car-dependent commuters were excluded. Based on psychometric factors, the support vector machine successfully separated the car-dependent commuters from normal-choice riders.

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.

A traffic analysis zone definition: a new methodology and algorithm

This paper develops a comprehensive approach to the definition of transportation analysis zones (TAZ), and therein, presents a new methodology and algorithm for the definition of TAZ embedded in geographic information systems software, improves the base algorithm with several local algorithms, and comprehensively analyses the obtained results. The results obtained are then compared to these presently used in the transportation analysis process of the Lisbon Metropolitan Area. The proposed algorithm presents a new methodology for TAZ design based on a smoothed density surface of geocoded travel demand data. The algorithm aims to minimise the loss of information when moving from a continuous representation of the origin and destination of each trip to their discrete representations through zones, and focuses on the trade-off between the statistical precision, geographical error, and the percentage of intra-zonal trips of the resulting OD matrix. The results for the Lisbon Metropolitan Area case study suggest a significant improvement in OD matrix estimates compared to current transportation analysis practises based on administrative units.

Railway reforms: do they influence operating efficiency?

This paper considers railway operations in 23 European countries during 1995–2001, where a series of reform initiatives were launched by the European Commission, and analyses whether these reform initiatives improved the efficiency of the railway systems. Efficiency is measured using Multi-directional Efficiency Analysis, which enables investigation of how railway reforms affect the inefficiencies of specific cost drivers. The main findings are that the reform initiatives generally improve technical efficiency but potentially differently for different cost drivers. Specifically, the paper provides empirical evidence that accounting separation is important for improving the efficiency in the use of both material and staff costs, whereas other reforms only influenced one of these factors.

Estimating the robustness of questionnaire results: lessons from a mixed-mode survey of expectations for tele-working and road-based business travel

The robustness of questionnaire results to various forms of bias are explored in the context of a dual-mode (web and hardcopy) survey of employers’ anticipations of levels of employee commuting and business travel activity under a range of future ICT scenarios. The questionnaire incorporated several innovative features which, together with the dual-mode format, allowed an unusually wide range of analyses. For example: the robustness of respondents’ opinions was tested by examining the effect of incorporating alternative versions of a briefing text, one being very positive and one very negative, about the role of ICT; instrument bias was identified via detailed comparison of the results from the two versions of the questionnaire; and the impact of exogenous factors which are often ignored or taken as constant was assessed via special supplementary questions. Analysis showed that the robustness of opinions and expectations varied and was influenced by respondent characteristics, and that results from the two versions of the questionnaire differed significantly. It is concluded that opinions and expectations are less robust, and questionnaire results are more subject to bias and myopic interpretation, than is generally recognised and that web-based surveys seem particularly vulnerable to sampling bias. Methods are suggested for measuring robustness, for reducing bias and for validating and contextualising results. The use of contrasting briefing texts is recommended as a means of establishing the robustness of opinions and expectations while supplementary questions are recommended for validating and contextualising SP and SE exercises.

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.

Studying travel-related individual assessments and desires by combining hierarchically structured ordinal variables

Ordinal measures are frequently encountered in travel behavior research. This paper presents a new method for combining them when a hierarchical structure of the data can be presumed. This method is applied to study the subjective assessment of the amount of travel by different transportation modes among a group of French clerical workers, along with the desire to increase or decrease the use of such modes. Some advantages of this approach over traditional data reduction technique such as factor analysis when applied to ordinal data are then illustrated. In this study, combining evidence from several variables sheds light on the observed moderately negative relationship between the personal assessment of the amount of travel and the desire to increase or decrease it, thus integrating previous partial (univariate) results. We find a latent demand for travel, thus contributing to clarify the behavioral mechanisms behind the induced traffic phenomenon. Categorizing the above relationship by transportation mode shows a desire for a less environmental-friendly mix of modes (i.e., a greater desire to use heavy motorized modes and a lower desire to use two-wheeled modes), whenever the respondents do not feel to travel extensively. This result, combined with previous theoretical investigations concerning the determinants of the desire to alter trips consumption levels, shows the importance of making people aware of how much they travel.

Evaluating the pedestrian realm: instrumental rationality,communicative rationality and phenomenology

Following the passage of ISTEA, increased attention to pedestrian planning has led to the development of pedestrian plans, particularly at the metropolitan and municipal levels. This has raised the issue of how cities and metropolitan areas evaluate the walkability of the pedestrian realm and identify improvement projects. Three approaches to evaluating the pedestrian realm are examined: instrumental rationality, communicative rationality, and phenomenology. Case studies demonstrating the application of these approaches to the development of pedestrian plans are examined in the Phoenix metropolitan area, Portland, Oregon, and Cambridge, Massachusetts.

Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

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.

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.

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.

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.