An empirical comparison of travel choice models that capture preferences for compromise alternatives

Compromise alternatives have an intermediate performance on each or most attributes rather than having a poor performance on some attributes and a strong performance on others. The relative popularity of compromise alternatives among decision-makers has been convincingly established in a wide range of decision contexts, while being largely ignored in travel behavior research. We discuss three (travel) choice models that capture a potential preference for compromise alternatives. One approach, which is introduced in this paper, involves the construction of a so-called compromise variable which indicates to what extent (i.e., on how many attributes) a given alternative is a compromise alternative in its choice set. Another approach consists of the recently introduced random regret-model form, where the popularity of compromise alternatives emerges endogenously from the regret minimization-based decision rule. A third approach consists of the contextual concavity model, which is known for favoring compromise alternatives by means of a locally concave utility function. Estimation results on a stated route choice dataset show that, in terms of model fit and predictive ability, the contextual concavity and random regret models appear to perform better than the model that contains an added compromise variable.     

An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: An integrated model of car type choice

Based on the data collected from a large-scale survey research of 1622 consumers, the present paper develops a disaggregate, compensatory choice model to collectively examine the impact of under-examined factors on consumer car type choice behaviour. All existing econometric forecasting models of vehicle type choice in the literature have so far considered objective measures as determinants of vehicle type choice. The proposed choice model considers 12 car-type alternatives and is successively extended to allow for choice probability distortions resulting from individual heterogeneity across a set of 30 variables, related to objective, behavioural and psychographic consumer characteristics. The results provide clear evidence that variables such as purpose of car use, prepurchase information source used, consumer’s proneness towards buying an ecological car, consumer’s involvement with cars, and consumer’s attachment to cars, significantly affect car type choice. The results yield important implications for manufacturers, transportation planners and researchers.     

An investigation of prediction errors in work trip mode choice models

This paper reports some analyses of predictive accuracy of disaggregate work trip mode choice models. The prediction error is separated into three components: model specification error, aggregation error, and transfer error. The main results are (1) the total forecasting errors can be very large, especially if the model transfers poorly; (2) poor transfer-ability was found between cities where the transit or shared ride markets are much different; (3) prediction from models having statistically different coefficients need not be much different and (4) the type of level-of-service data, i.e. manually coded or network based, used in model estimation has little influence on forecasting accuracy. These and other results are provisional for reasons discussed in the main text.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Comparative analysis of traveler destination choice models by method of sampling alternatives

In a destination choice model, it is important to introduce alternatives that have been adequately aggregated into traffic analysis zone levels based on spatial similarities and feasibility of analysis, because considering every spatial location possible for the traveler as an elemental alternative is intractable in terms of data management and analysis. In this study, we derive strata for alternative sets through simple random sampling and stratified importance sampling based on the concept of Moran’s I. As a result of comparative analysis, we are able to reduce errors by drawing an adequate number of samples for the destination choice model’s choice alternative sets based on measures of spatial similarity.     

Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models

In order to understand the mode shift behavior of car travelers and relieve traffic congestion, a Stated Preference survey has been conducted in the city of Ji'nan in China to analyze bus choice behavior and the heterogeneity of car travelers. Several discrete choice models, including multinomial logit, mixed logit and latent class model (LCM) are developed based on these survey data. A comparative analysis indicates that the LCM has the highest precision and is more suitable to analyze the heterogeneity of car travelers. The LCM divides car travelers into three classes. Different classes have different sets of influencing factors in the model. Policy recommendations are also proposed for those classes to promote bus shift from car travelers based on the model results. Finally, sensitivity analysis on parking fees and fuel cost is carried out on the LCMs under different bus service levels. Car travelers have different sensitivities to the influencing factors. The conclusions indicate that the LCM can reflect the heterogeneity and preferences of car travelers and can be used to understand how to shift the behavior of car travelers and make more effective traffic policy.     

Short‐term travel speed prediction models in car navigation systems

The objective of this study is the development of the short‐term prediction models to predict average spot speeds of the subject location in the short‐term periods of 5, 10 and 15 minutes respectively. In this study, field data were used to see the comparison of the predictability of Regression Analysis, ARIMA, Kalman Filtering and Neural Network models. These field data were collected from image processing detectors at the urban expressway for 17 hours including both peak and non‐peak hours. Most of the results were reliable, but the results of models using Kalman Filtering and Neural Networks are more accurate and realistic than those of the others.     

Specification of the cross-nested logit model with sampling of alternatives for route choice models

We present an operational estimation procedure for the estimation of route choice multivariate extreme value (MEV) models based on sampling of alternatives. The procedure builds on the state-of-the-art literature, and in particular on recent methodological developments proposed by Flötteröd and Bierlaire (2013) and Guevara and Ben-Akiva (2013b). Case studies on both synthetic data and a real network demonstrate that the new method is valid and practical.     

Place happiness: its constituents and the influence of emotions and subjective importance on activity type and destination choice

The way in which a person organizes his or her day, both temporally and spatially, is a highly important matter to travel behavior and travel demand modeling. Many times, the focus of these models is to accurately predict the “where” and “when”, without paying adequate attention to the “why.” The participation in activities, and therefore the selection of a place for these activities has been recently discussed within the framework of subjective well being. The motivation of happiness can be used to understand how and why people make the choices that they do. Many different criteria are used by individuals in the selection of destinations. These criteria range from attributes such as distance and cost, to attributes such as comfort, security and social aspects in determining the most rewarding destinations. Aspects contributing to a rewarding experience can also be viewed as those decision criteria that lead to the highest satisfaction. In this paper, several attributes of places and decision-making are explored for their potential to explain destination choices. First, a broader analysis of destination choice and criteria used helps us develop a geographic representation of attitudes and views regarding the area of Santa Barbara, California. Following this general evaluation of space, individual activity types are statistically analyzed in the importance different attributes play in the selection of a destination that leads to higher satisfaction.     

Correcting for endogeneity in behavioral choice models with social influence variables

While psychologists and behavioral economists emphasize the importance of social influences, an outstanding issue is how to capture such influences in behavioral models used to inform urban planning and policy. In this paper we focus on operational models that do not require explicit knowledge of the individual networks of decision makers. We employ a field effect variable to capture social influences, which is calculated as the percent of population in the peer group that has chosen the specific alternative. We define the peer group based on socio-economic status and spatial proximity of residential location. As in behavioral economics and psychology, the concept is that one is influenced by the choices made by one’s peers. However, using such a social influence variable in a behavioral model causes complications because it is likely endogenous; unobserved factors that impact the peer group also influence the decision maker, yielding correlation between the field effect variable and the error. The contribution of this paper is the use of the Berry, Levinsohn, and Pakes (BLP) method to correct the endogeneity in a choice model. The two-stage BLP introduces constants for each peer group to remove the endogeneity from the choice model (where it is difficult to deal with) and insert it into a linear regression model (where endogeneity is relatively easier to deal with). We test the method using a mode choice data set from the Netherlands and readily available software and find there is an upward bias of the field effect parameter when endogeneity is not corrected. The procedure outlined presents a practical and tractable method for incorporating social influences in choice models.     

Some errors in macroscopic traffic models based on car following models

The macroscopic traffic flow models developed from the car following models of Gazis et al. (1961) are shown to have a flaw in that they do not meet certain of the boundary conditions that researchers have said that they do. This does not affect many existing models but, nevertheless, should be cleared up.     

Sampling of alternatives in Logit Mixture models

Employing a strategy of sampling of alternatives is necessary for various transportation models that have to deal with large choice-sets. In this article, we propose a method to obtain consistent, asymptotically normal and relatively efficient estimators for Logit Mixture models while sampling alternatives. Our method is an extension of previous results for Logit and MEV models. We show that the practical application of the proposed method for Logit Mixture can result in a Naïve approach, in which the kernel is replaced by the usual sampling correction for Logit. We give theoretical support for previous applications of the Naïve approach, showing not only that it yields consistent estimators, but also providing its asymptotic distribution for proper hypothesis testing. We illustrate the proposed method using Monte Carlo experimentation and real data. Results provide further evidence that the Naïve approach is suitable and practical. The article concludes by summarizing the findings of this research, assessing their potential impact, and suggesting extensions of the research in this area.     

Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand

Random utility models are undoubtedly the most used models for the simulation of transport demand. These models simulate the choice of a decision-maker among a set of feasible alternatives and their operational use requires that the analyst is able to correctly specify this choice-set for each individual.Some early applications basically ignored this problem by assuming that all decision-makers chose from the same pre-specified choice-set. This assumption may be unrealistic in many practical cases and cause significant misspecification problems (P. Stopher, Transportation Journal of ASCE 106 (1980) 427; H. Williams, J. Ortuzar, Transportation Research B 16 (1982) 167).The problem of choice-set simulation has been dealt within the literature following two basically different approaches:

• •simulating the perception/availability of an alternative implicitly in the choice model,
• •simulating the choice-set generation explicitly in a separate model.

The implicit approach is more convenient from an operational point of view, while the explicit one is more appealing from a theoretical point of view.In this paper, a different approach to the modeling of availability/perception of alternatives in the context of random utility model is proposed. This approach is based on the concept of intermediate degrees of availability/perception of each alternative simulated through a model (or “inclusion function”) which in turn is introduced in the systematic utility of standard random utility models.This model, named implicit availability/perception (IAP), may be differently specified depending on assumptions made on the joint distribution of random residuals and the way in which the average degree of availability/perception is modeled.In this paper, a possible specification of the IAP model, based on the assumption of random residual distributed as i.i. Gumbel and with the average degree of availability/perception modeled as a binomial logit, is proposed.The paper also proposes ML estimation models in two cases: in the first, only information on alternatives choices is available, while in the second, this information is complemented with others on variables related to a latent (i.e., non-observable) alternatives availability/perception degree (e.g., information on car availability of decision-maker i used as an indirect measurement of the unknown and non-observable availability/perception degree of alternative car for decision-maker i in a modal split).The proposed specification is tested on mode choice data; the calibration results are compared with those of a similar logit specification with encouraging results in terms of goodness of fit.     

The meaning of car availability in mode choice decisions

The paper examines various definitions of car availability that have been used in the literature, and compares the results when applied to a common data set. It argues that car availability means different things to different people depending on their licence holding/car owning status. Using in‐depth interview data, the factors determining car allocation and transferability within a household, and subsequently mode choice, are discussed. An attempt is made to draw some general conclusions for research and modelling. This suggests that different approaches are required depending on whether or not there is competition for use of the car within a household. Also that in cases of competition, mode choice is not a two‐stage process depending on car availability and trip characteristics, but a concurrent decision based on both of these factors.     

Forecasting automobile petrol demand in Australia: An evaluation of empirical models

Transport fuel consumption and its determinants have received a great deal of attention since the early 1970s. In the literature, different types of modelling methods have been used to estimate petrol demand, each having methodological strengths and weaknesses. This paper is motivated by an ongoing need to review the effectiveness of empirical fuel demand forecasting models, with a focus on theoretical as well as practical considerations in the model-building processes of different model forms. We consider a linear trend model, a quadratic trend model, an exponential trend model, a single exponential smoothing model, Holt’s linear model, Holt–Winters’ model, a partial adjustment model (PAM), and an autoregressive integrated moving average (ARIMA) model. More importantly, the study identifies the difference between forecasts and actual observations of petrol demand in order to identify forecasting accuracy. Given the identified best-forecasting model, Australia’s automobile petrol demand from 2007 through to 2020 is presented under the “business-as-usual” scenario.     

Vehicle type choice under the influence of a tax reform and rising fuel prices

Differentiated vehicle taxes are considered by many a useful tool for promoting environmentally friendly vehicles. Various structures have been implemented in several countries, e.g. Ireland, France, The Czech Republic, and Denmark. In many countries the tax reforms have been followed by a steep change in new vehicle purchases toward more diesel vehicles and more fuel-efficient vehicles. The paper analyses to what extent a vehicle tax reform similar to the Danish 2007 reform may explain changes in purchasing behaviour. The paper investigates the effects of a tax reform, fuel price changes, and technological development on vehicle type choice using a mixed logit model. The model allows a simulation of the effect of car price changes that resemble those induced by the tax reform. This effect is compared to the effects of fuel price changes and technology improvements. The simulations show that the effect of the tax reform on fuel efficiency is similar to the effect of rising fuel prices while the effect of technological development is much larger. The conclusion is that while the tax reform appeared in the same year as a large increase in fuel efficiency, it seems likely that it only explains a small part of the shift in fuel efficiency that occurred and that the main driver was the technological development.     

Social networks,social influence and activity-travel behaviour: a review of models and empirical evidence

ABSTRACT

The study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research.     

Link choice proportions from trip distribution and assignment models: An overview and comparison

A number of estimation procedures have been suggested for the situation where a prior estimate of an origin-destination matrix is to be updated on the basis of recently-acquired traffic counts. These procedures assume that both the link flows and the proportionate usage of each link made by each origin-destination flow (referred to collectively as the link choice proportions) are known. This paper examines the possibility and methods for estimating the link choice proportions. Three methods are presented: (1) using ad hoc iteration between trip distribution and traffic assignment; (2) combining trip distribution and assignment in one step; (3) solving a new optimization problem in which the path flows are directly considered as variables and its optimal solution is governed by a logit type formula. The algorithms, covergencies and computational efficiencies of these methods are investigated. Results of testing the three methods on example networks are discussed.     

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.