Logit Mode Choice Model Research Articles

Modeling urban taxi services with multiple user classes and vehicle modes

This paper extends the model of urban taxi services in congested networks to the case of multiple user classes, multiple taxi modes, and customer hierarchical modal choice. There are several classes of customers with different values of time and money, and several modes of taxi services with distinct combinations of service area restrictions and fare levels. The multi-class multi-mode formulation allows the modeling of both mileage-based and congestion-based taxi fare charging mechanisms in a unified framework, and can more realistically model most urban taxi services, which are charged on the basis of both time and distance. The introduction of multiple taxi modes can also be used to model the differentiation between luxury taxis and normal taxis by their respective service areas and customer waiting times. We propose a simultaneous mathematical formulation of two equilibrium sub-problems for the model. One sub-problem is a combined network equilibrium model (CNEM) that describes the hierarchical logit mode choice model of occupied taxis and normal traffic, together with the vacant taxi distributions in the network. The other sub-problem is a set of linear and nonlinear equations (SLNE), which ensures the satisfaction of the relation between taxi and customer waiting times, the relation between customer demand and taxi supply for each taxi mode, and taxi service time constraints. The CNEM can be formulated as a variational inequality program that is solvable by means of a block Gauss–Seidel decomposition approach coupled with the method of successive averages. The SLNE can be solved by a Newtonian algorithm with a line search. The CNEM is formulated as a special case of the general travel demand model so that it is possible to incorporate the taxi model into an existing package as an add-on module, in which the algorithm for the CNEM is built in practice. Most of the parameters are observable, given that such a calibrated transport planning model exists. A numerical example is used to demonstrate the effectiveness of the proposed methodology.

Forecasting the effects of road user charge by stochastic agent-based modelling

This paper develops a new agent-based simulation model to improve discrete choice analysis as well as to analyse the effects of a road user charging scheme for the Upper Derwent Valley in the Peak District National Park. The advantages of discrete choice analysis are well known. However, results with these conventional methods can be biased if interaction effects are significant. The combined approach of the Minority Game, in which agents try to choose the option of the minority side, and discrete choice analysis is appropriate to deal with the problem. The main data was collected by stated preference survey. The agent-based model has four sub-modules: (1) multinomial mixed logit model for mode choice, (2) binary logit model for parking location choice, (3) Markov queue model for parking network, and (4) the Minority Game for parking congestion and learning. The results show that the road user charging scheme reduces car demand in the Upper Derwent Valley. The model also shows that an exemption will increase the utility of elderly visitors. In conclusion, the simulation model demonstrated that oversimplification in conventional discrete choice analysis gave significant biases when real world problems were analysed.

Network Effects in Public Transit Use: Evidence from a Spatially Autoregressive Mode Choice Model for New York

This article empirically tests for positive network effects in transit use by applying a spatial autoregressive logit mode choice model with 1997/98 work trip data from New York City. Positive network effects exist when people prefer to use transit together with other people as a result of social spill-over. Although these network preferences should differ for each person, because of statistical restrictions in the model, individual network preferences cannot be obtained. However, it is possible to derive econometrically a measure of aggregate network preference. This paper can be seen in the wider context of other recent work focusing on the analysis of spatial dependencies and network effects in travel.

Toll Demand Model for the Delaware Department of Transportation Travel Demand Model

As transportation providers across the nation face growing funding shortfalls, they are increasingly looking to tolling as an option to increase revenue. As toll revenue becomes a more important part of the funding for transportation programs, agencies are seeking to determine the potential impacts of more complex arrays of tolling options such as electronic toll collection and frequency-of-use discounts. The transportation planning organizations responsible for providing information to agencies are being expected to produce more reliable forecasts of traffic and revenue that directly reflect the subtleties of these tolling scenarios. To provide this information, the Delaware Department of Transportation (DelDOT) developed an improved toll demand model. This toll demand model was incorporated into the existing nested logit mode choice model. One thing that makes the new toll demand model unique is that it not only incorporates the toll–no-toll choice but also includes an E-ZPass ownership model used for cash–E-ZPass market segmentation. The model allows DelDOT to reduce the amount of “unquantifiable” attributes included in the mode choice bias constants. The model would be readily transferable to other areas that either do not currently have tolling or do not explicitly account for tolling in their existing models.

Mode Choice Behavior of Elderly Travelers in Honolulu, Hawaii

A literature review of studies that focused on the travel behavior of older people suggested that the following factors affected travel behavior: age, gender, medical condition, ability to drive, cost of a trip, residential location (suburb versus city), trip purpose, day of the week, time of day, income, and availability of a private vehicle. This study estimated two discrete-choice multinomial logit models of mode choice by using data from a household interview survey conducted by the Oahu Metropolitan Planning Organization, Hawaii. In addition to traveler and trip attributes, the models included certain travel time and cost variables. The estimation data contained data for trips made by both elderly and nonelderly people. These models suggest that being elderly, retired, or both, has a negative effect on driving, being a passenger, and walking, as compared with taking transit. Use of the estimated models to perform sensitivity analyses also is illustrated. This analysis helped to visualize the finding that being elderly and the costs of driving and out-of-vehicle transit are more important determinants of shifts away from driving and toward the use of transit than travel time changes.

Modeling of Motorcycle Ownership and Commuter Usage: A UK Study

This paper presents work, undertaken for the UK Department for Transport, to help determine how policy could affect motorcycle usage. There are two important choices that determine potential motorcycle use: the decision to own a motorcycle and, contingent on that, the decision to use a motorcycle for a particular trip. This research has addressed both of these, and this paper describes the development of models that represent these decision processes. The motorcycle ownership model predicts the number of motorcycles that a person owns and the engine sizes of these motorcycles, depending on the characteristics of the person and the average purchase cost. The structure of the ownership model is a disaggregate nested logit model, with structural parameters used to measure the sensitivity of the choice of engine size relative to motorcycle ownership. Existing travel surveys contained insufficient information with which to model the mode choice decisions of motorcycle owners. Therefore, new surveys that incorporated stated preference discrete choice experiments were designed. This also allowed the collection of data to examine how motorcycle usage may change as a result of policy and the impacts of other important influences, such as weather. The data were used to develop nested logit models of mode choice. These models also give some insight into how the ability to interlane filter influences mode choice. This is the first study from the United Kingdom that models both motorcycle ownership and mode choice. It provides useful insights for policy makers and illustrates the potential for the modeling of motorcycles within the same framework used other transport modes.

MODAL CHOICE MODELS ESTIMATION USING MIXED REVEALED AND STATED PREFERENCES DATA

Discrete choice demand models estimation can be performed using data on travel behavior obtained from revealed preference (RP) and stated preference (SP) surveys. RP techniques, traditionally utilized, are relative to the actual user's travel behavior in a real context. SP techniques are methodologies based on statements made by interviewees about their preferences in different choice contexts: real, hypothetical, or experimental. Therefore, a key innovation is introduced in this work: the consideration of choice alternatives not available at the time of the surveys. These techniques have been adopted for demand models calibration to predict the choices made by users and their preferences variations while the choice context changes. In particular, some multinomial logit mode choice models have been specified and calibrated. The calibrated models have been distinguished in: RP models, based on the choices made by users exclusively in the real context; SP models, based on the choices stated in the hypothetical contexts; and joint RP/SP models, using RP and SP data on the same sample. The study confirmed the utility of SP techniques for users' travel behavior analysis in a hypothetical choice context; moreover, it has confirmed that RP/SP conjoint analysis improves parameter estimation in discrete choice models.

Assessing Impact of Urban Form Measures on Nonwork Trip Mode Choice After Controlling for Demographic and Level-of-Service Effects

The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice have been investigated. The travel data used for analysis are from the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song in 2002 and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment by using the Portland data. The results of the multinomial logit mode choice model indicate that mixed uses promote walking behavior for nonwork activities.

Jointly Estimated Cross-Sectional Mode Choice Models: Specification and Forecast Performance

This paper investigates a number of issues associated with jointly estimating disaggregate logit mode choice models for two periods using data collected at two points in time in independent cross-sectional travel surveys in a given urban area. These include: (1) the effect socio-economic characteristics of travelers have on the predictive and forecast performance of jointly estimated models; and (2) the effect of allowing the variance of the random utilities in the different time periods to differ and, more broadly, the impact transfer-bias scale parameters could have on joint-model predictive performance. The results show that well-specified jointly estimated models using data from two time periods yield comparable disaggregate and aggregate forecasts to those obtained from conventional forecasting models, estimated with data from a single cross-sectional survey. Socio-economic variables and transfer-bias scale parameters are found to enhance model fit to estimation data as well as precision of predictions. The shorter the intervening period between when the two cross-sectional data sets used in joint estimation are collected. the better the jointly estimated models are able to predict the travel choices in each of the survey years.

Incorporating Nonmotorized Modes in a Mode Choice Model

A binary logit mode choice model that incorporates nonmotorized modes of transport was formulated. The model was calibrated with data collected at a Brazilian university campus, by means of travel diaries that were completed by students, staff, and faculty. The binary logit model gives the probability of an individual’s choosing walking or a motorized mode based on socioeconomic variables, land use characteristics, and trip length.

Methodology for Assessing High-Occupancy Toll-Lane Usage and Network Performance

A modeling framework for the evaluation of high-occupancy toll (HOT) facilities is discussed. It is based on a traffic simulation-assignment model interfaced with an unordered multinomial logit mode-choice model. The model allows for the prediction of high-occupancy, transit, and single-occupancy vehicle modal shares, based on prevailing traffic conditions and tolls. Also presented is an illustrative investigation of the sensitivity to various elements of the modal-choice function using the methodology and of the effect of different HOT facility features on HOT lane usage and associated system performance. These features include HOT lane configurations, pricing levels, carpooling attractiveness, changing departure times, and sensitivity to generalized cost. A representation of the south-central part of I-35W and the supporting roadways in the Fort Worth, Texas, area was used in the study. The results of the simulations were analyzed under each of the operating and behavior characteristics to evaluate the potential success of an HOT facility. Improving the carpooling attractiveness decreases the number of vehicles on the roadway but may not necessarily improve the average travel time for the system as a whole. The larger the departure-time window, the easier it is for travelers to carpool; however, the relatively small number of people choosing the HOV mode may inhibit the average travel time for the system from reflecting the impacts of the reduced vehicle trips. Increasing the sensitivity to time, or generalized cost, raises the HOV mode share when there is an HOT facility available.

Comparison of Neural and Conventional Approaches to Mode Choice Analysis

This paper describes a new approach to behavioral mode choice modeling using neuro-fuzzy models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. In addition, the approach only selects the variables that significantly influence the mode choice and that display the stored knowledge in terms of fuzzy linguistic rules. This allows the modal decision-making process to be examined and understood in great detail. The neuro-fuzzy model is tested on the U.S. freight transport market using information on individual shipper and individual shipments. Shipments are disaggregated at the five-digit Standard Transportation Commodity Code level. Results obtained form this exercise are compared with similar results obtained from the conventional logit mode choice model and the standard back-propagation artificial neural network. The advantages of using the neurofuzzy approach are described.

Analysis of the Role of Traveler Attitudes and Perceptions in Explaining Mode-Choice Behavior

An analysis was carried out using the 1991 wave of the Puget Sound Transportation Panel data set to determine the role played by attitudinal and preference variables in explaining commuter mode-choice behavior. Different modal market segments were compared to determine the extent to which attitudes and preferences differ across mode choices. A factor analysis was performed on the sample to identify a few distinct factors that would summarize the multitude of attitudinal variables present in the data set. Multinomial logit models of mode choice were estimated using different utility specifications. Three types of models were estimated: one that included only demographic variables, another that included only attitudinal factors, and another that included both demographic and attitudinal variables. Likelihood ratio tests were applied to assess the significance of the contribution of different types of variables in explaining mode-choice behavior. Results show that demographic variables and attitudinal variables are extremely important in explaining mode-choice behavior. More noteworthy, however, is the finding that the contribution of attitudinal factors is greater than that of demographic variables, thus emphasizing the need for greater consideration of attitudinal and preference variables in travel-demand-modeling applications.

Demand, supply, and pricing in urban road transport: The case of Ho Chi Minh City, Vietnam

Traffic and household survey data from 1992–1996 are used to derive the optimal congestion toll for major arterials in Ho Chi Minh City (HCMC), Vietnam. Speed-Flow Diagrams were derived for HCMC’s four main arterials. A multinomial logit model of mode choice was estimated using three separate data sets to find commuter’s value of time. An aggregate trip generation model was estimated to find the price elasticity of demand. Congestion tolls, potential revenues, and welfare benefits were calculated. Specifically, results show a value of time (VOT) of 45,559 Dong (around 2–3 times wage), and a price elasticity of demand of −.88. The optimal congestion tolls per kilometer were found for different vehicles. Estimated revenues from pricing the arterials would be US$5.7–14.2 million a year, as compared to current expenditures of around US$12 million a year. The welfare benefits of time savings alone are US$550,000-US$3.5 million a year. This estimate does not include savings from the efficient use of fuel, reduced pollution, reduced health costs, and the improved quality of life. Optimal congestion tolls may be derived in a fairly easy and inexpensive way. Moreover, the welfare and revenue benefits may be large, even for low income countries.

A disaggregate Box-Cox Logit mode choice model of intercity passenger travel in Germany and its implications for high-speed rail demand forecasts

We show that enriching logit mode choice model specification by mode attributes, socio economic variables and trip purpose characteristics significantly improves model quality, and that Box-Cox transformations applied to model attributes imply an asymmetry of the reaction curve, as well as more reasonable properties (diminishing marginal values of time savings, elasticities and values of time that differ among the modes) than those of the linear logit model. Moreover, the model yielded very different high speed rail market shares for Germany than results obtained with the usual linear utility functions.

Assessment of Intermodal Transfer Penalties Using Stated Preference Data

Since the passage of the Intermodal Surface Transportation Efficiency Act of 1991 there has been an increasing interest in the planning and design of an intermodal passenger transportation system. It has long been recognized that modal transfer has a certain penalty associated with it. The recent surge in intermodal planning merits an in-depth examination and accurate measurement of the penalties associated with transfers between modes. Current planning procedures usually involve an ad hoc treatment of transfer penalties based on various assumptions of wait time and value of time. To better assess the disutility associated with modal transfers, discrete choice models are used to quantify transfer penalties and their effects on mode choice in different transfer contexts. Revealed and stated preference data from the New York–New Jersey commute corridors are used to estimate logit models of mode choice reflecting the impacts of modal transfers. The model results suggest that the penalty factor associated with transfer time should be higher than that traditionally used in travel demand models and that the value of the transfer penalty varies according to the type of modal transfer.

A Disaggregate Mode-Choice Model Considering the Interdependencies among the Trips in a Trip Chain

Each person's travel behaviour in a day can be described in terms of linked trips or trip chains. Existing models have the shortcoming of not representing the interdependencies among the trips in a trip chain. In this paper a logit model of mode choice based on the utility theory considering the interdependencies of modal choice among the trips in a trip chain is developed.Finally this model is applied to estimate time-of-day travel volumes by mode.

Estimating a mode choice model with incomplete data

This study develops and uses a synthetic data base to calibrate a logit mode choice model of work trips in metropolitan Vancouver. Missing survey data entries for perceived measures of travel time and waiting time by bus, as well as operating and parking cost by car, are calculated using statistical methods to increase the survey sample of 275 complete cases to 621 usable cases. The synthesized data set is used to specify random utility functions for two planning assumptions. The short-term policy specification using only level of service variables does not produce a usable model, but the specification based on a long-term planning assumption using a combination of level of service and socioeconomic variables produces plausible results. The inconclusive results from the policy model could be due to survey data problems, data simulation, and (or) the lack of conceptual validity of perceived measures of transportation attributes. The planning model provides insight into mode split prediction and transportation management for cities that are undergoing dynamic demographic and social changes. Key words: mode choice, incomplete data, socioeconomic factors, logit model.

Discrete choice models and the housing price and travel to work elasticities of location demand

Multinomial logit and nested logit models of mode choice in travel to work and housing location choice are estimated from 1970 U.S. census data aggregated to small zones of the Chicago SMSA. The estimated models are then used to derive the “housing rent,” “travel time,” and “travel cost” elasticities of location demand. The effects of sampling variation, sample size, attribute inclusion, model specification, and estimation method on the estimated elasticities are evaluated and found to be important. The elasticities are also compared and found to agree with those obtained from other discrete choice models and, in the case of “housing rent,” with estimates obtained from models based on other theoretical structure.