Noisy demand and mode choice

Mode choice under stochastically varying demand is studied via a dynamic mathematical model which describes the behavioural interactions between population groups. The model is developed by assuming competing attractivity functions for automobile and public transit which motivate their use subject to an overall demand for transportation. When this demand is allowed to vary stochastically, a set of stochastic differential equations describing the model are obtained. These are solved for their steady-state values. It is found that noisy demand can structure the system qualitatively differently than when the demand is fixed. The noise is found to generally reduce the level of public transit ridership, but it also changes the values of the threshold at which new regimes occur and, most interestingly, it induces new steady-state solutions for ridership at critical values of the variance of demand. In the latter case, noise becomes a source of new possibilities in the system by triggering a steady-state solution not present in the noise-free environment.     

Regulatory reform and freight mode choice

Reform of trucking Vehicle Weights and Dimensions (VWD) regulations in Canada is now underway. The effect these reforms will have on the freight transportation industry are only recently the subject of research. This paper is part of this new research effort, aimed at understanding how regulatory reform in the trucking sector will affect the existing competitive relationship between trucks and the railways. The paper presents the results of study and research into modelling the relationship between mode choice and the service characteristics that are affected by VWD reform.Using several periods of data, a series of econometric models are developed which help to elucidate the relative relationships between the mode service characteristics for both of the principal interprovincial freight modes. A technique is developed and presented to model railway user costs in order to overcome the complex and often unrepresentable pricing activities of Canadian railways.The strength of the developed econometric models is presented, including their significance and statistical reliability. This is further reinforced by the similarities exhibited by all the models in the series. The selected model is applied to predict market service responses required of the railway industry in order to compete with the trucking sector now adapting to the new regulations.The impact of the newly implemented vehicle weight and dimension reform on the rail transportation industry is analyzed and railway industry improvements aimed at maintaining its market share are presented. The results predicted by the model show that railway user cost reductions should be moderate, and likely less than the level of inflation over the period of time when trucking converts its fleet to take advantage of the new regulations.Abbreviations AASHTO American Association of State Highway and Transportation Officials - CCMTA Canadian Conference of Motor Transport Administrators - EPI End products, inedible commodity classification - GVW Gross Vehicle Weight - NA 1988 VWD National Agreement - RTAC Roads and Transportation Association of Canada - VWD Vehicle Weight(s) and Dimension(s) - WA 1988 VWD Western Agreement     

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.     

A Stated preference freight mode choice model

Transport policy aims to assist the transport system to work more efficiently and effectively. An understanding of the reasons why people choose to move freight in a certain manner is critical to the development of appropriate policies. This article outlines a data collection approach and the development of a disaggregate mode choice model applicable to the analysis of freight shipper decision making. It focuses on the choice between rail and road in Java, Indonesia. The model indicates that safety, reliability and responsiveness are major attributes influencing rail/road freight mode choice. Transport policies aimed at improving these dimensions should increase the attractiveness of rail transport.     

The sensitivity of parameter estimates to data specification in mode choice models

Standard network data are generally used in estimation of mode choice models. These data are inaccurate in several ways, but the cost of correcting the inaccuracies is great. This paper analyzes the effects which correcting some of the inaccuracies in the standard network data has on the estimated parameters of mode choice models. Models are estimated on the standard network data and on data which have been adjusted so as to correct the problems in the standard network data. It is found that, for analysis of policies affecting transfer wait times or distances to bus stops, correction of the standard network data is advisable. For other policy analyses, however, it seems that the extra expense of correcting the standard data is unnecessary.     

Transportation mode choice and city-suburban public transportation service

A model is presented to describe the dynamics of transportation mode choice in which the interaction between transportation users and a public transportation authority results in self-organization. The model illustrates that a sufficient number of connections between a central city and its suburbs are required for self-organization to occur whereby public transportation use and service will grow.     

Model development for metro station access mode choice

This paper analyses the behaviour of metro users in choosing their access mode to a metro station. Multinominal logit models with satisfactory predictive power were developed for access mode choice on the basis of data collected by interviewing metro users at existing metro stations. A population segmentation approach was adopted and models referring to individuals having the same set of alternative access modes were developed. Trip purpose was found to have significant effects on the access mode choice. Thus, for each population segment different models are proposed for work and education and other trip purpose. Various conclusions concerning the importance of the variables included in the proposed models were drawn through comparisons carried out across the models.     

Addressing endogeneity in strategic urban mode choice models

Transportation - Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that...     

Modelling commuting mode choice with explicit consideration of carpool in the choice set formation

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).     

Gender inequality in mobility and mode choice in Pakistan

Using the nationally representative dataset of the 2007 Pakistan Time-Use Survey, this paper examines gender differences in daily trip rate, mode choice, travel duration, and purpose of travel, which are previously unreported because of limited data availability. Wide gender mobility gaps are observed in the country, where women are less likely to travel, are half as mobile as men and may rely heavily on walking. The particular social and cultural context of the country, that renders women as private, secluded and family honor, seems influential in shaping their mobility and choice of activities. Demographic factors such as age, household income, and marital status significantly decrease female mobility levels. Hence, these findings call for a gender-based culturally responsive transportation policy in the country.     

Effects of increased awareness on choice of travel mode

This paper reports a field experiment with the purpose of studying the effects of increased awareness on travel mode choice. One hundred fifteen subjects were randomly assigned to an experimental and a control group. In the experimental group, a more deliberate choice of travel mode was induced and expected to result in a stronger relationship between attitude and behavior, a weaker relationship between habit and behavior, and a behavioral change among individuals with a strong habit. Attitude, habit, and behavior were measured in travel diaries and questionnaires. The results indicated no significant change in the relationship between attitude and behavior and no significant change in the relationship between habit and behavior. However, a temporally extended decrease in car use was observed in the experimental group. The effect was noted for individuals with a strong habit who reduced their car use but not for subjects with a weak habit.     

Development and validation of a direct mode choice model

A direct discrete mode choice model is introduced using relative attributes of competing modes as well as socioeconomic characteristics of travelers. The model is calibrated and validated for two available historic databases in the Dallas–Fort Worth region. The validation is conducted against the outputs of a current nested logit model used by the regional planning organization as well as the observed values based on transit ridership surveys for a newly inaugurated commuter rail service. The calibrated model is applied after the introduction of this new transit mode. The results show that the estimated mode shares by the proposed model have a statistically better consistency with the observed values than the estimates of the conventional nested logit model. Unlike the logit model, the structure of the direct model based on relative attributes also has the advantage of not needing recalibration each time a new travel mode is introduced. The model is found to be easier to calibrate and produces more accurate results than the nested logit model, commonly used by many metropolitan planning organizations.     

Characteristics of premium transit services that affect mode choice

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.     

Integrating congestion pricing,transit subsidies and mode choice

We model and analyze optimal (welfare maximizing) prices and design of transport services in a bimodal context. Car congestion and transit design are simultaneously introduced and consumers choose based on the full price they perceive. The optimization variables are the congestion toll, the transit fare (and hence the level of subsidies) and transit frequency. We obtain six main results: (i) the optimal car-transit split is generally different from the total cost minimizing one; (ii) optimal congestion and transit price are interdependent and have an optimal frequency attached; (iii) the optimal money price difference together with the optimal frequency yield the optimal modal split; (iv) if this modal split is used in traditional stand-alone formulations – where each mode is priced independently–resulting congestion tolls and transit subsidies and fares are consistent with the optimal money price difference; (v) self-financing of the transport sector is feasible; and (vi) investment in car infrastructure induces an increase in generalized cost for all public transport users.     

How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model

Transportation - This study attempts to develop a comprehensive framework by integrating the theory of planned behavior (TPB) and latent class choice model, with aim to understanding how mode-use...     

The inverse power transformation logit and dogit mode choice models

This paper explores the properties of inverse Box-Cox and Box-Tukey transformations applied to the exponential functions of logit and dogit mode choice models. It is suggested that inverse power transformations allow for the introduction of modeler ignorance in the models and solve the “thin equal tails” problem of the logit model; it is also shown that they allow for asymmetry of response functions in both logit and dogit models by introducing alternative-specific parameters which make cross elasticities of demand among alternatives generally asymmetric. In the dogit model, modeler ignorance and consumer captivity remain conceptually distinct. Standard logit and dogit models appear as very special “perfect knowledge” cases in broad spectra of models which also include, among others, the reciprocal extreme value or log-Weibull variants. These improvements over the simple symmetric-thin-equal-tail-perfect-knowledge logit and the symmetric-pure-captivity dogit are achieved at the cost of introducing at the most two new parameters per alternative considered in the original logit and dogit mode choice models.     

Some estimation results of a simultaneous model of auto ownership and mode choice to work

This paper describes the theory, development and estimation of a simultaneous disaggregate model of automobile ownership and mode to work choices. The motivation for such a model and the general theory of the simultaneous probabilistic choice model are briefly discussed. The general model specification and the set of choices assumed to be available to each household is then considered. Finally, the variables used in the model are defined and the estimation results are presented.     

A simultaneous destination and mode choice model for shopping trips

This paper describes a disaggregate simultaneous destination and mode choice model for shopping trips. Following an introduction to the model structure and a review of the data, the results of five different model specifications are discussed. The models were estimated using data from two communities adjacent to Eindhoven, the Netherlands and utilise the multinomial logit model.     

The influence of employer ridesharing programs on employee mode choice

Employer ridesharing programs and employee mode choice were analyzed using Southern California data. Problems in estimating the costs and benefits of employer ridesharing programs were identified. Surveyed firms used a wide variety of information to estimate employee mode split internally. Virtually all surveyed firms offered free or subsidized parking to some or all of their employees. Few responding firms estimated the cost of providing employee parking accurately, if at all. Despite these significant data limitations, factors influencing firm choice of employer ridesharing program components were identified. The influence of employer ridesharing programs on employee mode choice was modeled using weighted least squares logit regression analysis. Firm size was foung to be the single most important variable identified in the analysis. Larger firms were more likely to offer ridesharing incentives to their empolyees, and to report direct employer benefits from ridesharing. Alternative work hours hindered the formation of ridesharing arrangements in some cases. Relatively few firms promoted ridesharing on a purely voluntary basis. A private market for employer ridesharing services was found to exist, however. Personalized matching assistance may be a critical factor in developing more effective employer ridesharing programs. Parking pricing and supply control measures probably would have a larger impact on employee mode split overall. Parking management faces severe obstacles in implementation, some of which might be overcome through the more extensive provision of ridesharing services, such as personalized matching assistance. to employees at specific employment sites by their employers.