A dynamic approach to road freight flows modeling in Spain

This paper presents an innovative approach to analyzing road vehicle freight traffic that uses a dynamic panel data specification derived from a gravity model. This dynamic approach, which has recently been employed in international goods trade models in lieu of the traditional static specification, is applied to the case of Spain using data for the countrys 15 NUTS-3 regions between 1999 and 2009. Using the system general method of moments approach, we obtained significant evidence that the flow of vehicles carrying commodities by road has a strong persistence effect when controlling for unobserved heterogeneity. We also found that the quality of road transport infrastructure has a significant impact on vehicle trips. According to our findings, we suggest that this type of specification be employed in distribution models in which fixed effects and lags of the dependent variable are included to account for unobserved heterogeneity and persistence effects, respectively.     

Testing probabilistic discrete choice models of travel demand by comparing predicted and observed aggregate choice shares

Probabilistic discrete choice models of travel demand often are tested for the presence of specification errors by comparing the models' predictions of aggregate choice shares in population strata with observed shares. A model is rejected as misspecified if the differences between its predictions and the observations are judged too large. This judgement usually is made on intuitive grounds without use of formal statistical methods and, therefore includes no systematic method for distinguishing the effects of specification errors on differences between predictions and observations from those of random sampling errors. This paper represents formal statistical tests for comparing predicted and observed aggregate chioce shares in population strata and reports the results of an investigation of the power of the tests. The test statistics are asymptotically χ² disturbed when the model being tested is correctly specified. The results of the power investigation suggests that greater power is obtained (i.e. there is ability to detect misspecified models) when all of the available data are used for both parameter estimation and specification testing than when the available data are divided into separate estimation and test data sets. Specification tests based on comparisons of predicted and observed aggregate choice shares appear to have less power than do likelihood ratio and likelihood ratio index specification tests when the alternative models required by the latter tests are correctly or approximately correctly specified. However, tests based on comparisons of predicted and observed shares ca have greater power than the other tests when the alternative models are seriouslymisspecified.     

Panel analysis in transportation planning: An overview

The advantages, disadvantages, and issues involved in the panel analysis of travel behavior are discussed. Increased statistical efficiency, possibility of improved prediction, and the ability to observe changes and examine behavioral dynamics, are among the advantages panel data offer. Their disadvantages, on the other hand, stem from the added biases and costs of panel survey, and increased complexity involved in the analysis. Following a discussion of response lags, leads, and other dynamic elements of travel behavior, the paper offers a brief review of statistical methods available for the analysis of panel data. Use of panel data and dynamic models for demand forecasting is then described, followed by sample size, interview frequency, and other considerations in panel survey design and administration.     

Modelling mode choice for freight transport using advanced choice experiments

In this paper we use advanced choice modelling techniques to analyse demand for freight transport in a context of modal choice. To this end, a stated preference (SP) survey was conducted in order to estimate freight shipper preferences for the main attributes that define the service offered by the different transport modes. From a methodological point of view, we focus on two critical issues in the construction of efficient choice experiments. Firstly, in obtaining good quality prior information about the parameters; and secondly, in the improved quality of the experimental data by tailoring a specific efficient design for every respondent in the sample.With these data, different mixed logit models incorporating panel correlation effects and accounting for systematic and random taste heterogeneity are estimated. For the best model specification we obtain the willingness to pay for improving the level of service and the elasticity of the choice probabilities for the different attributes. Our model provide interesting results that can be used to analyse the potential diversion of traffic from road (the current option) to alternative modes, rail or maritime, as well as to help in the obtaining of the modal distribution of commercial traffic between Spain and the European Union, currently passing through the Pyrenees.     

Dynamic analysis of trip generation

A number of models are presented and estimated describing the correlation of trip making over time. Unobserved heterogeneity is taken into account using random effects. The basic models considered are the serial correlation and the state-dependence model. Trip making in total and by transit was best described using state-dependence models; trip making by car by a model with lagged exogenous variables. The generalized methods of moments procedure is used for estimation of the models: it is asymptotically efficient and does not require assumptions about the initial conditions.     

Identification and diagnosis of specification errors in the multinomial logit model

Multinomial logit models of travel demand are subject to a variety of specification errors that can severely affect the accuracy the models' forecasts. This paper describes some of the important sources of specification errors in logit models, presents numerical illustrations of the errors' effects on forecasts of travel behavior, and evaluates the ability of several informal and formal statistical procedures to identify and diagnose the errors. Among the tests considered, the most powerful are a test against a probit model and the McFadden, Tye and Train (1976) test based on the universal logit method. The least powerful tests are a test based on extrapolating the logit model and informal tests based on examination of the signs, t-statistics and ratios of the estimated parameters of the logit function.     

Understanding variability,habit and the effect of long period activity plan in modal choices: a day to day,week to week analysis on panel data

Understanding variability in individual behaviour is crucial for the comprehension of travel patterns and for the development and evaluation of planning policies. But, with only one notable exception, there are no studies on the intrinsic variability in the individual preferences for mode choices in absence of external changes in the transport infrastructures. This requires using continuous panel data. Few papers have studied mode choice with continuous panel data but mainly focused on the panel correlation. In this work we use a six-week travel diary survey to study the intrinsic variability in the individual preferences for mode choices, the effect of long period plans and habitual behaviour in the daily mode choices. Mixed logit models are estimated that account for the above effects as well as for systematic and random heterogeneity over individual preferences and responses. We also account for correlation over several time periods. Our results suggest that individual tastes for time and cost are fairly stable but there is a significant systematic and random heterogeneity around these mean values and in the preferences for the different alternatives. We found that there is a strong inertia effect in mode choice that increases with (or is reinforced by) the number of time the same tour is repeated. The sequence of mode choice made is influenced by the duration of the activity and the weekly structure of the activities     

A framework for analyzing rank-ordered data with application to automobile demand

In this paper, we develop a general random utility framework for analyzing data on individuals’ rank-orderings. Specifically, we show that in the case with three alternatives one can express the probability of a particular rank-ordering as a simple function of first choice probabilities. This framework is applied to specify and estimate models of household demand for conventional gasoline cars and alternative fuel vehicles in Shanghai based on rank-ordered data obtained from a stated preference survey. Subsequently, the framework is extended to allow for random effects in the utility specification to allow for intrapersonal correlation in tastes across stated preference questions. The preferred model is then used to calculate demand probabilities and elasticities and the distribution of willingness-to-pay for alternative fuel vehicles.     

Stockholm congestion charging: an assessment with METROPOLIS and SILVESTER

This paper reviews and compares the performance of two dynamic transportation models – METROPOLIS and SILVESTER – which are used to predict the impacts of congestion charging for Stockholm. Both are mesoscopic dynamic models treating accumulation and dissipation of traffic queues, route choice, modal split and departure time choice. The models are calibrated independently for the baseline situation without charges and applied to forecast the effects of congestion charging. The results obtained from the two models are mutually compared and validated against the actual outcome of the Stockholm congestion charging scheme. Both models successfully predict the outcomes of the congestion charging trial at both aggregate and disaggregate levels. Results of welfare analysis, however, differ substantially due to differences in model specification.     

The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis

The efficiency of the container port industry has been variously studied utilising either Data Envelopment Analysis (DEA) or Stochastic Frontier Analysis (SFA). Given the strengths and weaknesses associated with these two approaches, the efficiency estimates and scale properties derived from these analyses are not always convincing. This paper applies both approaches to the same set of container port data for the world’s largest container ports and compares the results obtained. A high degree of correlation is found between the efficiency estimates derived from all the models applied, suggesting that results are relatively robust to the DEA models applied or the distributional assumptions under SFA. High levels of technical efficiency are associated with scale, greater private-sector participation and with transhipment as opposed to gateway ports. In analysing the implications of the results for management and policy makers, a number of shortcomings of applying a cross-sectional approach to an industry characterised by significant, lumpy and risky investments are identified and the potential benefits of a dynamic analysis, based on panel data, are enumerated.     

Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system

This paper examines two heuristic rules proposed for describing urban commuters' predictions of travel time as well as the adjustments of departure time in response to unacceptable arrivals in their daily commute under limited information. It is based on the notion that the magnitude of the predicted travel time depends on each commuter's own experience, including recallable travel time, schedule delay, and difficulties in searching for a satisfactory departure time. An explanatory analysis is first performed to compare these two rules, based on the information provided by a set of commuters interacting over 24 days through a simulated traffic system. A more elaborate model specification which captures the dynamic interrelation between the commuter's cumulative and recent experience with the traffic system's performance is then proposed. The model parameters are estimated with explicit consideration of the serial correlation arising from repeated decisions by the same individuals and the contemporaneous interaction with other system users' decisions through the traffic system's performance.     

A nonparametric approach to the incorporation of heterogeneity into repeated polytomous choice models of urban shopping behaviour

Longitudinal discrete choice models which use a mixing distribution to control for unobserved heterogeneity, and which are estimated by a nonparametric maximum likelihood mass point procedure, have so far been restricted to the analysis of dichotomous choices. This paper extends mass point methods to the case of polytomous choice problems and in so doing makes an important contribution to the analysis of longitudinal data and dynamic discrete choice models. The paper concludes with an empirical assessment of the performance of mass point methods in the analysis of urban shopping behaviour, using short-term panel data from the Cardiff Consumer Panel.     

Which road do I take? A learning-based model of route-choice behavior with real-time information

This paper presents a learning-based model of route-choice behavior when information is provided in real time. In a laboratory controlled experiment, participants made a long series of binary route-choice trials relying on real-time information and learning from their personal experience reinforced through feedback. A discrete choice model with a Mixed Logit specification, accounting for panel effects, was estimated based on the experiment’s data. It was found that information and experience have a combined effect on drivers’ route-choice behavior. Informed participants had faster learning rates and tended to base their decisions on memorization relating to previous outcomes whereas non-informed participants were slower in learning, required more exploration and tended to rely mostly on recent outcomes. Informed participants were more prone to risk-seeking and had greater sensitivity to travel time variability. In comparison, non-informed participants appeared to be more risk-averse and less sensitive to variability. These results have important policy implications on the design and implementation of ATIS initiatives. The advantage of incorporating insights from Prospect Theory and reinforced learning to improve the realism of travel behavior models is also discussed.     

Urban travel demand: The impact of Box-Cox transformations with nonspherical residual errors

We develop an algorithm for solving regression models with Box-Cox transformations on both the dependent and independent variables, while simultaneously taking into account corrections for serial correlation of several orders and for heteroscedasticity. The latter correction is of a general form which contains as special cases most specifications of heteroscedasticity found in practice. We apply the procedure to three urban travel demand functions, two of which are currently used in their linear form by the Montreal Transit Authority, and analyze more than 100 specifications. Our results show that taking into account nonsphericalness of the residuals has a major impact on model parameter estimates, notably on those which determine the functional form of the model, and that, conversely, modifications of the functional form have strong implications for both the structure of autocorrelation and the importance of heteroscedasticity; moreover, we find interactions between autocorrelation and heteroscedasticity structures. We introduce a special measure of elasticity for variables which contain zero observations, particularly dummy variables. Moreover, we find that elasticities of demand and implicit values of time depend to a large extent on the stochastic specification of the model.     

Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

Road segments with identical site-specific attributes often exhibit significantly different crash counts due to unobserved reasons. The extent of unobserved heterogeneity associated with a road feature is to be estimated prior to selecting the relevant safety treatment. Moreover, crash count data is often over-dispersed and spatially correlated. This paper proposes a spatial negative binomial specification with random parameters for modeling crash counts of contiguous road segments. The unobserved heterogeneity is incorporated using a finite multi-variate normal mixture prior on the random parameters; this allows for non-normality, skewness in the distribution of the random parameters, facilitates correlation across the random parameters, and relaxes any distributional assumptions. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. The specification simultaneously accounts for potential spatial correlation of the crash counts from neighboring road segments. A Gibbs sampling framework is proposed that leverages recent theoretical developments on data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models. Empirical results suggests the presence of two latent groups and spatial correlation within the study road network. Road features with significantly different effect on crash counts across two latent groups of road segments were identified.     

Forecasting Freight Flows

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment. This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates constitute a valuable input to freight models for forecasting future capacity problems.     

Empirical test of a constrained choice discrete model: Mode choice in São Paulo,Brazil

This paper examines the properties and empirically tests a model of discrete choice which incorporates probabilistic choice set generation. Denominated the Parametrized Logit Captivity (PLC) model, it is a generalization of the well-known “dogit” specification. The PLC model is shown to be theoretically and empirically more flexible than the latter. Work mode choice data collected in a 1977 O/D survey in São Paulo, Brazil, is used to obtain parameter estimates, as well as to evaluate consumer reaction to a series of perturbations in travel time, travel cost and income, for both the PLC and Multinomial Logit models. Comparisons between the two specifications are made in terms of statistical fit, reasonableness of predictions and differences in predictions across models.     

Towards a dynamic discrete-choice model of household automobile fleet size and composition

The emphasis on energy consumption in studies of traveller behaviour has led to increased interest in the development of policy sensitive models of automobile demand. In recognition of the fuller dimensions of automobile demand, a number of studies have considered choice amongst types of automobiles as well as number of automobiles. With rare exception, existing studies have concentrated on either type choice or number choice. In all instances the approach has been static. This paper develops a series of linked discrete-choice models to explain household automobile holdings (type and number) and adjustments in the holdings over time. The empirical study is part of an initial data effort leading up to the development of a full scale longitudinal panel of Sydney households. A model system based on a retrospective panel of 354 households, interviewed in 1980, is reported herein. The model is dynamic in the sense that it allows for prior decisions, brand loyalty and the costs of transacting.     

The <Emphasis Type="Italic">Santiago Panel</Emphasis>: measuring the effects of implementing Transantiago

Although panels offer significant advantages over cross sectional data especially in terms of evaluating the effects of significant policy changes, there are precious few examples of panels built around an important change to an urban transport system. For this reason we took the opportunity of the introduction of Transantiago, a radically new public transport system for Santiago de Chile in February 2007, to form a panel, the first wave of which was taken in December 2006. The final objective was to use this Santiago Panel to estimate mode choice models considering both inertia and policy effects. This document describes both the design and construction of the panel, and presents some results based on an analysis of its four waves; for example, we registered a high percentage of mode change (55.1%) attributable to the introduction of the new system. The panel can claim the highest response rate (or lowest attrition) reported in the literature (95% in the second wave, 92% in the third one, and 85% in the forth one). This hints at the possibility of developing sophisticated models to evaluate the effects of a system shock in the presence of inertia in decision making.     

Modeling the competition among air-travel itinerary shares: GEV model development

This study reports the results of aggregate air-travel itinerary share models estimated using data from all East West markets in the United States and Canada. These models predict airline ridership at the itinerary level and aid carriers in long and intermediate term decision-making. Official and comprehensive schedule and bookings data is used to estimate generalized extreme value models capturing the inter-itinerary competition dynamic along three dimensions: time of day, carrier and level-of-service (nonstop, direct, single-connect, double-connect). Models incorporate one, two or three of these dimensions simultaneously. Model structures considered include multinomial logit and variations of the nested logit model (two-level nested logit, two-level weighted nested logit, three-level nested logit, three-level weighted nested logit and nested weighted nested logit). Independent variables for the models measure various itinerary service characteristics such as level-of-service, connection quality, carrier attributes, aircraft type, and departure time. Additionally, the advanced models yield inverse logsum and/or weight parameter estimates capturing the underlying competitive dynamic among air-travel itineraries. The results are intuitive, and the advanced models outperform the more basic specifications with regard to statistical tests and behavioral interpretations, giving insight into the competitive dynamic of air-carrier itineraries.