Trucking industry adoption of information technology: a multivariate discrete choice model

The objective of this research is to understand the demand for information technology among trucking companies. A multivariate discrete choice model is estimated on data from a large-scale survey of the trucking industry in California. This model is designed to identify the influences of each of twenty operational characteristics on the propensity to adopt each of seven different information technologies, while simultaneously allowing the seven error terms to be freely correlated. Results showed that the distinction between for-hire and private fleets is paramount, as is size of the fleet and the provision of intermodal maritime and air services.     

A flexible model structure approach for discrete choice models

Multi-dimensional discrete choice problems are usually estimated by assuming a single-choice hierarchical order for the entire study population or for pre-defined segments representing the behavior of an “average” person and by indicating either limited differences or a variety in choices among the study population. This study develops an integral methodological framework, termed the flexible model structure (FMS), which enhances the application of the discrete choice model by developing an optimization algorithm that segment given data and searches for the best model structure for each segment simultaneously. The approach is demonstrated here through three models that conceptualize the multi-dimensional discrete choice problem. The first two are Nested Logit models with a two-choice dimension of destination and mode; they represent the estimation of a fixed-structure model using pre-segmented data as is mostly common in multi-dimensional discrete choice model implementation. The third model, the FMS, includes a fuzzy segmentation method with weighted variables, as well as a combination of more than one model structure estimated simultaneously. The FMS model significantly improves estimation results, using fewer variables than do segmented NL models, thus supporting the hypothesis that different model structures may best describe the behavior of different groups of people in multi-dimensional choice models. The implementation of FMS involves presenting the travel behavior of an individual as a mix of travel behaviors represented by a number of segments. The choice model for each segment comprises a combination of different choice model structures. The FMS model thus breaks the consensus that an individual belongs to only one segment and that a segment can take only one structure.     

Analysis of personal action space using a model system with multiple choice structures

The concept of heterogeneity in choice structure is applied in this study to examine individuals’ action space, which is defined in terms of the reported frequencies of visits. A disaggregate model system that predicts the frequency of visits is condensed using a nested logit model with multiple choice structures.     

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.     

Analysis of equilibrium automobile holdings in Israel with aggregate discrete choice models

Models of household vehicle ownership decisions do not suffice as a basis for forecasting the size and composition of aggregate vehicle holdings. Forecasting applications require that such models be imbedded in systems describing the operation of the automobile market. This paper presents a new model of short run equilibrium in the automobile market. The short run is a period within which new car designs and prices are fixed but used car prices adjust competitively to market forces. The magnitude and mix of new car sales, the extent of used car scrappage and the composition of used car holdings are determined in equilibrium with used car prices. An econometric version of the market model has been estimated on Israeli data and applied to analyze the impact of vehicle tax policy on automobile holdings in Israel. The paper describes this application.     

Statistical models of discrete choice processes

A number of problems studied in the transportation literature, such as automobile choice, motorist route choice, and transportation mode choice, involve an agent who makes a series of discrete choices over time. This paper presents statistical models and estimation methods for such discrete choice processes, and illustrates potential applications of these methods to the transportation literature.     

Fundamentals of discrete multimodal choice modelling

Abstract

In this paper an overview is given of the most relevant issues relating to the application of multimodal choice models, with particular emphasis on disaggregate modal split models. The paper considers questions of data, such as type of data, alternative sampling strategies and problems of measurement; and modelling issues, such as model specification and estimation, including a good presentation of the statistical techniques'available. The paper also addresses the aggregation problem, which lies at the heart of one of today's most hotly contested debates: whether to use aggregate or disaggregate models for policy analysis, and in which circumstances.     

Random covariance heterogeneity in discrete choice models

In this paper, we extend the standard discrete choice modelling framework by allowing for random variations in the substitution patterns between alternatives across respondents, leading to increased model flexibility. The paper shows how such a Mixed Covariance model can be specified either with purely random variation or with a mixture of random and deterministic variation. Additionally, the model can be based on an underlying GEV or ECL structure. Finally, the model can be specified as a continuous mixture or as a discrete mixture. An application on Stated Preference data for the choice of departure time and travel mode shows that important gains in model performance can be obtained by allowing for random covariance heterogeneity. Furthermore, the approach leads to significant differences in the implied willingness to pay measures, and the substitution patterns between alternatives.     

Accounting for stochastic variables in discrete choice models

The estimation of discrete choice models requires measuring the attributes describing the alternatives within each individual’s choice set. Even though some attributes are intrinsically stochastic (e.g. travel times) or are subject to non-negligible measurement errors (e.g. waiting times), they are usually assumed fixed and deterministic. Indeed, even an accurate measurement can be biased as it might differ from the original (experienced) value perceived by the individual.Experimental evidence suggests that discrepancies between the values measured by the modeller and experienced by the individuals can lead to incorrect parameter estimates. On the other hand, there is an important trade-off between data quality and collection costs. This paper explores the inclusion of stochastic variables in discrete choice models through an econometric analysis that allows identifying the most suitable specifications. Various model specifications were experimentally tested using synthetic data; comparisons included tests for unbiased parameter estimation and computation of marginal rates of substitution. Model specifications were also tested using a real case databank featuring two travel time measurements, associated with different levels of accuracy.Results show that in most cases an error components model can effectively deal with stochastic variables. A random coefficients model can only effectively deal with stochastic variables when their randomness is directly proportional to the value of the attribute. Another interesting result is the presence of confounding effects that are very difficult, if not impossible, to isolate when more flexible models are used to capture stochastic variations. Due the presence of confounding effects when estimating flexible models, the estimated parameters should be carefully analysed to avoid misinterpretations. Also, as in previous misspecification tests reported in the literature, the Multinomial Logit model proves to be quite robust for estimating marginal rates of substitution, especially when models are estimated with large samples.     

Red-light cameras at intersections: Estimating preferences using a stated choice model

Red-light cameras placed at intersections have the potential to increase safety, but they are often viewed as an invasion of privacy. Preferences for these cameras were explored using a stated choice model that presents key attributes of camera placements. Stated choice models involve careful experimental design, akin to experimental control in laboratory settings. A variety of design approaches were used, settling on a composition of the choice sets people face in the survey. To illustrate the approach, an internet survey was used with a convenience sample containing a high percentage of college students. The results show that while not the case independently, as the number of cameras and fines for violators are simultaneously increased, the preferences for one particular red light cameras program are likely to improve.     

Identifying variations and co-variations in discrete choice models

This study introduces an extended version of a standard multilevel cross-classified logit model which takes co-variations into account, i.e., variations jointly caused by two or more unobserved factors. Whilst focusing on mode choice behavior, this study deals with four different types of variation: spatial variations, inter-individual variations, intra-individual variations and co-variations between inter-individual and spatial variations. Such co-variations represent individual-specific spatial effects, reflecting different responses to the same space among individuals, which may for example be due to differences in their spatial perceptions. In our empirical analysis, we use data from Mobidrive (a continuous six-week travel survey) to clarify the existence of co-variation effects by comparing two models with and without co-variation terms. The results of this analysis indicate that co-variations certainly exist, especially for utility differences in bicycle and public transport use in comparison with car use. We then sequentially introduce four further sets of explanatory variables, examine the sources of behavioral variations and determine what types of influential factors are dominant in mode choice behavior.     

Global versus localised attitudinal responses in discrete choice

Despite the increasing popularity of including attitudinal and perceptual indicators within discrete choice models, debate endures as to whether there exists a causative relationship between attitudes and behaviour, resulting in what has been termed the attitude behaviour gap. In attempt to understand its origins, attitudes have been categorised as global or localised according to whether or not they are related to a specific time, context and action. Under this framework, global attitudes (GA) typically result in poor predictions of specific overt behaviours, whilst attitudes toward behaviour, or localised attitudes (LA), tend to be better predictors of actual outcomes. Also, attitude strength, measured as the accessibility in memory, plays a determinant role in reducing the gap between attitudes and behaviour, with “memory-based” attitudes having a better prediction of overt behaviours than short-term attitudes constructed “on the spot”. The specific focus of the current paper is to examine the temporal stability and the nature of attitudes, being it critical to transportation planning and research considering the controversial link between attitudes and behaviour. An in depth analysis of the different types of attitudes towards satisfaction for train trips reveals that GAs and LAs provide moderately different outcomes. Also, a memory effect has been found, suggesting the connection between attitudes created on the spot and those stored in memory. Further, both GAs and LAs impact significantly on individual preferences. Finally, the omission of LAs, which are rarely employed within transport literature, may potentially lead to inconsistent estimates, as their contribution in explaining the choice will be absorbed by the error term.

     

A solution algorithm for a dynamic deterministic user equilibrium assignment model with departure time choice

Abstract

A route-based combined model of dynamic deterministic route and departure time choice and a solution method for many origin and destination pairs is proposed. The divided linear travel time model is used to calculate the link travel time and to describe the propagation of flow over time. For the calculation of route travel times, the predictive ideal route travel time concept is adopted. Solving the combined model of dynamic deterministic route and departure time choice is shown to be equivalent to solving simultaneously a system of non-linear equations. A Newton-type iterative scheme is proposed to solve this problem. The performance of the proposed solution method is demonstrated using a version of the Sioux Falls network. This shows that the proposed solution method produces good equilibrium solutions with reasonable computational cost.     

A stochastic transit assignment model using a dynamic schedule-based network

Using the schedule-based approach, in which scheduled time-tables are used to describe the movement of vehicles, a dynamic transit assignment model is formulated. Passengers are assumed to travel on a path with minimum generalized cost which consists of four components: in-vehicle time; waiting time; walking time; and a time penalty for each line change. With the exception of in-vehicle time, each of the other cost components is weighted by a sensitivity coefficient which varies among travelers and is defined by a density function. This time-dependent and stochastic minimum path is generated by a specially developed branch and bound algorithm. The assignment procedure is conducted over a period in which both passenger demand and train headways are varying. Due to the stochastic nature of the assignment problem, a Monte Carlo approach is employed to solve the problem. A case study using the Mass Transit Railway System in Hong Kong is given to demonstrate the model and its potential applications.     

A dynamic route choice model for public transport networks with boarding queues

Abstract

The concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, …, k-th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms.     

Generation of synthetic datasets for discrete choice analysis

Despite the widespread use of synthetic data in discrete choice analysis, little is known about how the methodology used to generate synthetic datasets influences the properties of parameter estimates and the validity of results based on these estimates. That is, there are two potential sources of biases when using synthetic discrete choice data: (1) bias due to the method used to generate the dataset; and, (2) bias due to parameter estimation. The primary objective of this study is to examine bias due to the underlying data generation method. This study compares three methods for generating synthetic datasets and uses design of experiments and analysis of variance methods to investigate the ability to recover estimates for “true” logsum parameters for nested logit models. The method that uses nested logit probabilities to generate the chosen alternative results in unbiased parameter estimates. The method that is based on Gumbel error component approximations reveals that while the error components themselves are unbiased, subtle empirical identification problems can arise when these error components are combined with synthetically generated utility functions. The method that is based on normal error component approximations reveals that all logsum coefficients are biased upwards; the bias dramatically increases for those nests that have a low choice frequency and is most pronounced for those nests with high correlations among alternatives. Based on the results of the analysis, several recommendations for the generation of synthetic datasets for discrete choice analyses are provided.     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in ?ilina, the Slovak Republic, as a case study     

Inferring proxy response in household travel surveys with unknown completer using a group-based choice model

Transportation - A proxy response is often accepted for household travel surveys to reduce the survey cost and increase the sample size, but proxy-response biases may be introduced into the sample...     

Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network

Transportation - One of the major objectives of this study is to provide more realistic and accurate results related to transit passenger’s route choice behavior by using population data of...