Calibration of multinomial logit models of mode and destination choice

Multinomial logit models of travel mode and destination choice are calibrated using a disaggregate data base. Previous practice is extended to investigate the effects on model parameter values of socio-economic standardization of tripmaking households for the journeys to work, shop and recreation. These models are calibrated at the regional scale. Values of travel time and travel demand elasticities are presented, and compared with the findings of previous studies in this field.     

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 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.     

Estimation of a constrained multinomial logit model

Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous process to define the alternatives choice/rejection mechanism. The model allows for the inclusion of multiple constraints and has a closed form. In this paper, we study the estimation of the CMNL model using the maximum likelihood function, develop a methodology to estimate the model overcoming identification problems by an endogenous partition of the sample, and test the model estimation with both synthetic and real data. The CMNL model appears to be suitable for general applications as it presents a significantly better fit than the MNL model under constrained behaviour and replicates the MNL estimates in the unconstrained case. Using mode choice real data, we found significant differences in the values of times and elasticities between compensatory MNL and semi-compensatory CMNL models, which increase as the thresholds on attributes become active.     

Evaluation of activity constrained choice sets to shopping destination choice modelling

Recent research indicates that the constraints on an individual's freedom to move through space and time, have an important influence on his likelihood to participate in various activities, and on the amount of time he budgets to them. To date, commonly used transportation planning processes have failed to incorporate the time budget concept into the various planning and policy evaluation stages. It seems as well, that one of the drawbacks of the disaggregate modelling process is its neglect of the effect of temporal constraints on an individual's choice process. The temporal constraints affect the size of choice sets of individuals and thus influence the probabilities of selection of the available alternatives. The core of this paper is to demonstrate how individual specific constraint choice sets can be defined in a destination choice model, and how they can be applied for improving accuracy in prediction. Emphasis is placed on the additional capabilities provided by the incorporation of the choice set definitions, both in terms of improved prediction accuracy, and in terms of evaluating policies, which affect the constraints on activity scheduling. A model is developed which uses knowledge concerning the constraints on shopping travel to generate choice sets of retail locations for consumers. This is done by calculating the maximum amounts of time which these constraints allow an individual to be at various retail outlets. Locations which cannot be reached while open are removed from the choice set. This model was applied to data from the Tel Aviv Metropolitan Area. The results show that applying choice set restrictions yields a modest improvement in the accuracy of prediction of shopping destination choice. Exploratory analyses indicate that by further restricting choice sets in an appropriate amnner, it may be possible to achieve substantial improvement in prediction accuracy.     

An analysis of destination choice for opaque airline products using multidimensional binary logit models

We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers.     

Factors influencing destination choice for the urban grocery shopping trip

Destination choice for the urban grocery shopping trip is hypothesized to be determined by three factors: the individual's perception of the destination, the individual's accessibility to the destination and the relative number of opportunities to exercise any particular choice. Results of a multinomial logit model estimation support this hypothesis and provide useful information concerning the role of urban form in this destination choice situation. It is determined that accessibility is the primary aspect influencing destination choice and that its effect is nonlinear.On leave 1977-78 from State University of New York at Buffalo, Buffalo, New York 14214.     

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.     

The accuracy of the multinomial logit model as an approximation to the multinomial probit model of travel demand

The multinomial probit model of travel demand is considerably more general but much less tractable than the better-known multinomial logit model. In an effort to determine the effects of using the relatively simple logit model in situations where the assumptions of probit modeling are satisfied but those of logit modeling are not, the accuracy of the multinomial logit model as an approximation to a variety of three-alternative probit models has been evaluated. Multinomial logit can give highly erroneous estimates of the choice probabilities of multinomial probit models. However, logit models appear to give asymptotically accurate estimates of the ratios of the coefficients of the systematic components of probit utility functions, even when the logit choice probabilities differ greatly from the probit ones. Large estimation data sets are not necessarily needed to enable likelihood ratio tests to distinguish three-alternative probit models from logit models that give seriously erroneous estimates of the probit choice probabilities. Inclusion of alternative-specific dummy variables in logit utility functions cannot be relied upon to reduce significantly the errors of logit approximations to the choice probabilities of probit models whose utility functions do not contain the dummies.     

Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python

The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.     

Development of destination choice model with pairwise district-level constants using taxi GPS data

In this paper, a destination choice model with pairwise district-level constants is proposed for trip distribution based on a nearly complete OD trip matrix in a region. It is found that the coefficients are weakly identified in a destination choice model with pairwise zone-level constants. Thus, a destination choice model with pairwise district-level constants is then proposed and an iterative algorithm is developed for model estimation. Herein, the “district” means a spatial aggregation of a number of zones. The proposed model is demonstrated through simulation experiments. Then, destination choice models with and without pairwise district-level constants are estimated based on GPS data of taxi passenger trips collected during morning peak hours within the Inner Ring Road of Shanghai, China. The datasets comprise 504,187 trip records and a sample of 10,000 taxi trips for model development. The zones used in the study are actually 961 residents’ committees while the districts are 52 residential districts that are spatial aggregations and upper-level administrative units of residents’ committees. It is found that the estimated value of time dramatically drops after the involvement of district-level constants, indicating that the traditional model tends to overestimate the value of time when ignoring pairwise associations between two zones in trip distribution. The proposed destination choice model can ensure its predicted trip OD matrix to match the observed one at district level. Thus, the proposed model has potential to be widely applied for trip distribution under the situation where a complete OD trip matrix can be observed.     

A multinomial logit car use model for a megacity of the developing world: Istanbul

The increase in motor vehicle use is one of the important factors that cause traffic congestion, especially in megacities. Thus, the reasons behind this increase require serious attention. This paper offers an analysis of this kind, for a megacity from the developing world, Istanbul. A stratified multinomial logit model accounting for the availability of a second vehicle in the household is estimated for a sample drawn from a questionnaire to gather information of actual car use in Istanbul. This estimation is only possible through a unique data generation process that converts actual preferences into a choice study setting. In addition, a simulation study, generally utilized in the analyses of discrimination between certain layers of society, and a scenario analysis related to changes in income are also included in the paper for a better understanding of the nature of the topic. The results show that the behavior of households with a second vehicle available and not available varies significantly due to household, individual and professional-related characteristics.     

Adaptive choice behaviour of motorists in congested shopping centre parking lots

Currently existing models of parking choice behaviour typically focus on the choice of types of parking spaces. Implicitly these models assume that motorists have a free choice in that spaces are available. The adaptive behaviour which they reveal when faced with congested parking spaces is not explicitly modelled. The aim of this paper is to contribute to the growing literature on parking choice modelling by developing and testing a stated choice model of adaptive behaviour of motorists who are faced with fully occupied parking lots. The findings of the analyses indicate that the model performs satisfactory as indicated by its goodness-of-fit and the fact that all significant parameters were in anticipated directions.     

Modeling co-dependent choice of workplace,residence and commuting mode using an error component mixed logit model

Transportation - This paper develops an error component mixed logit model to analyze the multi-dimensional residential, work and transportation mode choice. It expanse previous studies based on...     

Hierarchical ordering of nests in a joint mode and destination choice model

This paper seeks to explore the relationship between mode and destination choice in an integrated nested choice model. A fundamental argument can be made that in certain circumstances, the ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. This results in a travel demand model where travelers are more likely to change destinations than to change transportation modes. For small and medium size urban areas, particularly in the United States, with less well developed public transit systems that draw few choice riders, this assumption makes much more sense than the traditional modeling assumptions. The models used in the new travel modeling system developed for Knoxville, Tennessee utilize this reversed ordering, with generally good results, which required no external tinkering in the logsum parameters.     

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.     

A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection

Residential location search has become an important topic to both practitioners and researchers as more detailed and disaggregate land-use and transportation demand models are developed which require information on individual household location decisions. The housing search process starts with an alternative formation and screening stage. At this level households evaluate all potential alternatives based on their lifestyle, preferences, and utilities to form a manageable choice set with a limited number of plausible alternatives. Then the final residential location is selected among these alternatives. This two-stage decision making process can be used for both aggregate zone-level selection as well as searching disaggregate parcel or building-based housing markets for potential dwellings. In this paper a zonal level household housing search model is developed. Initially, a household specific choice set is drawn from the entire possible alternatives in the area based on the average household work distance to each alternative. Following the choice set formation step, a discrete choice model is utilized for modeling the final residential zone selection of the household. A hazard-based model is used for the choice set formation module while the final choice selection is modeled using a multinomial logit formulation with a deterministic sample correction factor. The approach presented in the paper provides a remedy for the large choice set problem typically faced in housing search models.     

Residential mobility and location choice: a nested logit model with sampling of alternatives

Residential mobility and relocation choice are essential parts of integrated transportation and land use models. These decision processes have been examined and modeled individually to a great extent but there remain gaps in the literature on the underlying behaviors that connect them. Households may partly base their decision to move from or stay at a current location on the price and quality of the available alternatives. Conversely, households that are on the market for a new location may evaluate housing choices relative to their previous residence. How and the degree to which these decisions relate to each other are, however, not completely understood. This research is intended to contribute to the body of empirical evidence that will help answer these questions. It is hypothesized that residential mobility and location choice are related household decisions that can be modeled together using a two-tier hierarchical structure. This paper presents a novel nested logit (NL) model with sampling of alternatives and a proposed procedure for sampling bias correction. The model was estimated using full-information maximum likelihood estimation methods. The results confirm the applicability of this NL model and support similar findings from other empirical studies in the residential mobility and location choice literature.     

A combined destination and route choice model for a bicycle sharing system

This paper studies the supply variables that influence the destination and route choices of users of a bicycle sharing system in the Chilean city of Santiago. A combined trip demand logit model is developed whose explanatory variables represent attributes relating to the topology of the possible routes and other characteristics such as the presence of bikeways, bus service and controlled intersections. The data for the explanatory variables and system users were collected through field surveys of the routes and interviews conducted at the system stations. The results of the model show that proximity to stops on the Santiago Metro and the existence of bikeways are the main factors influencing destination and route choices. Also indicated by the model estimates are gender differences, a preference for tree-lined routes and an avoidance of routes with bus services. Finally, the outcomes reveal considerable potential for the integration of bicycle sharing systems with Metro transit.