Efficiency of a highway use reservation system for morning commute

This paper examines the design and efficiency of a highway use reservation system where commuters need reservations to access a highway facility at specific times. We show that, by accommodating reservation requests to the level that the highway capacity allows, traffic congestion can be relieved. Generally, a more differentiated design of the reservation system yields a higher reduction of travel cost and thus achieves a higher efficiency. The efficiency bound of the system is established. We also show that braking or tactical waiting behaviors of drivers would cause a loss of efficiency, which thus need be proactively accommodated. Given that user heterogeneity cause further loss of efficiency, we explore how two specific types of user heterogeneity affect the system efficiency. Auction-based reservation is then proposed to mitigate the efficiency loss.     

Assessment of motor vehicle use characteristics in three Indian cities

Estimates of emissions and energy consumption by vehicular fleet in India are not backed by reliable values of parameters, leading to large uncertainties. We report new methods, including primary surveys and secondary data sources, to estimate in-use fleet size, annual mileage (kilometers per year), and fuel efficiency of cars and motorised two-wheelers (MTW) for Delhi, and except fleet size and annual mileage of cars, for Visakhapatnam and Rajkot. We estimated that the official number of registered cars and MTW in Indian cities is more than two times the actual number of in-use vehicles. The private vehicular fleet in India is the youngest, its fuel efficiency one of the highest, and annual kilometers travelled is the lowest, compared to many high-income countries, such as the USA and those in European Union. Along with high renewal rate of fleet, the data suggest that it is possible for India to have one of the most fuel-efficient vehicle fleets in the world in the future, if fuel-efficiency standards and fiscal policies to contain growing dieselization are implemented in the country at the earliest.     

A system dynamics approach to land use / transportation system performance modeling Part II: Application

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model system consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is also examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited to some extent due to data availability, the results clearly indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling     

A system dynamics approach to land use/transportation system performance modeling Part I: Methodology

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited due to data availability, the results indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling.     

Trip chaining as a barrier to the propensity to use public transport

Trip chaining is a growing phenomenon in travel and activity behaviour. Individuals increasingly seek out opportunities to minimise the amount of travel required as part of activity fulfilment, given the competing demands on time budgets and their valuation of travel time savings. This search for ways of fulfilling (more) activities with less travel input has produced a number of responses, one of which is trip chaining. A particularly important policy implication of trip chaining is the potential barrier it creates in attracting car users to switch to public transport. This paper seeks to improve our understanding of trip chaining as a barrier to public transport use. A series of discrete choice models are estimated to identify the role that socio-economic and demographic characteristics of households have on the propensity to undertake trip chains of varying degrees of simplicity/complexity that involve use of the car or public transport with an embedded commuting or non-commuting primary purpose. Multinomial logit, nested logit and random parameter logit models are developed and contrasted to establish the gains in relaxing the strict conditions of the multinomial logit model.     

Decision tree method for modeling travel mode switching in a dynamic behavioral process

As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers’ mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers’ switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.     

The role of intention as mediator between latent effects and behavior: application of a hybrid choice model to study departure time choices

Transportation - An increasing number of papers are focusing on integrating psychological aspects into the typical discrete choice models. The majority of these studies account for several latent...     

Assessing asymmetric response effect of behavioral intention to service quality in an integrated psychological decision-making process model of intercity bus passengers: a case of Taiwan

This study introduces the concept of loss aversion to consumer behavioral intention at the personal psychological level to develop an integrative structural equation model for analyzing traveler psychological decision making. In this model, the relationship between behavioral intention and service quality is a non-smooth function based on the theory of loss aversion. The expectation service quality in the SERVQUAL model proposed by Parasuraman, Zeithaml, and Berry (PZB) serves as a reference point. This model can be applied to analyze the effect of non-smooth response of behavioral intention to service quality in a traveler psychological decision-making process model. Intercity travel among cities in Taiwan is used as an empirical example. Data were gathered in cities in Taiwan via a questionnaire survey, and the model was tested using path analysis performed by LISREL. The empirical result shows that all causal relationships are statistically significant. Service quality loss influences repurchase intention more than does Service quality gain. Finally, this study concludes by discussing managerial implications and suggesting directions for future research.

Changes in travel mode use after residential relocation: a contribution to mobility biographies

In recent years, a growing body of research has been emerging that focuses on changes in travel behaviour over an individual’s life course. It has been labelled the ‘mobility biographies approach’ and highlights changes in travelling induced by key events and experiences in an individual’s life course. In this context residential relocation plays an important role. This paper examines changes in travel mode use after residential relocations using structural equation modelling. It draws on retrospectively recorded empirical data collected in the region of Cologne. The findings show that relocations and associated changes in the built environment induce significant changes in car ownership and travel mode use and thus may be regarded as key events in an individual’s mobility biography. Changes in levels of satisfaction with attributes of the built environment have a significant impact in this context as well. The causal direction of the changes fulfils expectations: suburbanisation is followed by increases in car use and decreases in public transport use, bicycle use and walking. The opposite is true for relocations into the city. In addition, changes in household structure that tend to go along with relocation have significant effects. The findings provide further evidence for the built environment having a causal impact on mode use: modal changes temporally follow changes in the built environment and thus appear to be adjustments to the new spatial setting.     

Carsharing: the impact of system characteristics on its potential to replace private car trips and reduce car ownership

Transportation - This paper aims to explore the potential of carsharing in replacing private car trips and reducing car ownership and how this is affected by its attributes. To that affect, a...     

Towards a theoretical model for behavioural adaptations to changes in the road transport system

A preliminary theoretical model is developed for the mechanism of behavioural adaptations to changes in the road transport system, which describes the process of behavioural change, explains the nature of adaptation phenomenon, and predicts its effects on road safety programmes. It can be used as a basis to organize the body of adaptation research and to explain empirical results.     

新型临时路面系统在深圳地铁车站施工中的应用

针对深圳地铁科学馆站盖挖工程,介绍了一种新型的临时路面支撑系统--军用梁的设计与施工概况.实践表明,这种临时路面系统的应用是成功的,取得了较好的社会效益和经济效益,对类似工程具有重要的参考价值.     

The importance of attitudes in the decision to use mass transit

This paper reports the results of tests of the hypotheses that attitudinal variables are important in mode choice decisions and that they can significantly increase the explanatory power of network-based mode choice models. Conflicts between the results of previous work by Lovelock and Johnson are resolved by this study. Attitudinal items used by Johnson and by Lovelock in separate studies in the San Francisco Bay area were included in a survey of Chapel Hill households. Tests of the incremental explanatory power of the attitudinal variables in mode choice models confirm that the items used by Johnson do not contribute to the explanatory power of models using network time and cost data. Similar tests showed that Lovelock's attitudinal items do significantly increase the predictive ability of the models. The conflicting results of these previous studies are therefore due to the content of the items. Attitudinal data, including both attitude items and measures of perceptions of system attributes, do enhance the predictive power of models involving network data.This research was supported by a grant from the Urban Mass Transportation Administration, U.S. Department of Transportation, Washington, D.C.     

Access to railway stations and its potential in increasing rail use

The propensity to travel by rail, and not, for example by car, can be considered to be a factor of the rail service offered, the access to it and the characteristics of the population served. Efforts to increase rail use usually focus on the rail service itself while the accessibility of the rail network receives less attention. In this context, the paper has two broad aims. First, to evaluate how important the ‘access-to-the-station’ part of a rail journey is to passengers in their overall satisfaction with the rail journey and second, to investigate the balance between characteristics of the service, the access to it and the population served in determining rail use in different parts of the rail network. The analysis is carried out for the Netherlands. To achieve the first aim, we use the Dutch Railways customer satisfaction survey and apply principal component analysis and derived importance techniques to assess the relative importance of accessibility in determining the overall satisfaction with the rail journey. For the second aim, we use regression analysis to explain, at the Dutch postcode level, the propensity to use rail. We find that satisfaction with the level and quality of the access to the station is an important dimension of the rail journey which influences the overall satisfaction from that journey and that the quality and level of accessibility is an important element in explaining rail use. The conclusion reached is that in many parts of the rail network improving and expanding access services to the railway station can substitute for improving and expanding the services provided on the rail network and that it is probably more cost efficient when the aim is to increase rail use. These parts of the network are mainly in the periphery where the current level of rail service is relatively low.     

Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach

In recent years we have seen an explosion of research seeking to understand the role that rules and heuristics might play in improving the predictive capability of discrete choice models, as well as delivering willingness to pay estimates for specific attributes that may (and often do) differ significantly from estimates based on a model specification that assumes all attributes are relevant. This paper adds to that literature in one important way—it explicitly recognises the endogeneity issues raised by typical attribute non-attendance treatments and conditions attribute parameters on underlying unobserved attribute importance ratings. We develop a hybrid model system involving attribute processing and outcome choice models in which latent variables are introduced as explanatory variables in both parts of the model, explaining the answers to attribute processing questions and explaining heterogeneity in marginal sensitivities in the choice model. The resulting empirical model explains how lower latent attribute importance leads to a higher probability of indicating that an attribute was ignored or that it was ranked as less important, as well as increasing the probability of a reduced value for the associated marginal utility coefficient in the choice model. The model does so by treating the answers to information processing questions as dependent rather than explanatory variables, hence avoiding potential risk of endogeneity bias and measurement error.     

Factors affecting the use of bus and rail services in a deregulated environment

In 1987, the NSW Government commenced deregulation of the long-distance bus industry in NSW. This immediately led to greater inter-modal competition and contestability within the context of changing passenger markets.This study utilises categorical data analysis methods to examine the emerging passenger markets of inter-modal competitors (bus and rail) and to assess the relative importance of socioeconomic and travel related variables which affect the use of bus and rail services along the high volume Sydney-Canberra and Sydney-North Coast corridors.Conclusions from the study indicate varied passenger markets within a relatively new contestable environment which are mode and corridor specific. Results are indicative of the need for competitors to develop marketing strategies conducive to the demands of the travelling public in order to enhance viability and commercial opportunities.     

Modelling the dynamics of bus use in a changing travel environment using panel data

Panel data offers the potential to represent the influence on travel choices of changing circumstances, past history and persistent individual differences (unobserved heterogeneity). A four-wave panel survey collected data on the travel choices of residents before and after the introduction of a new bus rapid transit service. The data shows gradual changes to bus use over the four waves, implying time was required for residents to become aware of the new service and to adapt to it. Ordered response models are estimated for bus use over the survey period. The results show that the influence of level of service (LOS) is underestimated if unobserved heterogeneity is not taken into account. The delayed response to the new service is able to be well represented by including LOS as a lagged variable. Current bus use is found to be conditioned on past bus use, but with additional influence of lagged LOS and unobserved heterogeneity. It is shown how different model specifications generate different evolution patterns with the most realistic predictions arising from a model which takes into account lagged responses to change in LOS and unobserved heterogeneity. The paper demonstrates the feasibility of developing panel data models that can be applied to forecasting the effect of interventions in the travel environment. Longer panels—encompassing periods of both stability and change—are required to support future efforts at modelling travel choice dynamics.     

Modeling car ownership and use in a developing country context with informal public transportation

Transportation - Car ownership and use is a main contributor to the deterioration of air quality in cities and to global warming. There is thus a pressing need to understand their determinants in...