Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

Relationship between service quality and demand for inter-urban buses

This article proposes a model for analysing the modal choice of travellers making inter-urban journeys. Discrete choice models with systematic and random tastes variation were applied to find the most relevant variables for encouraging the use of public transport by bus rather than private car. This research follows on from the results of previous user satisfaction studies on inter-urban bus services in the province of Burgos (Spain). Willingness to pay is also estimated for time savings or other improvements in the bus service.The results indicate that, in general, improvements in the journey time or the number of daily journeys are valued less by inter-urban bus users than they are by car or railway users. The type of bus and its characteristics are evaluated as a function of the distance travelled and result in very small values for this variable. Contrary to what is often reported in satisfaction surveys, the journey cost is found to be relevant when choosing which mode of transport to use, but the most important variable is journey time. Little value is placed on the features of the bus, except on long distance journeys.Finally, a methodology differentiating four hierarchical groups is presented for comparing the results of the relevant variables in demand and satisfaction models. Some only improve perception rather than encourage new users, while others increase take-up but do not improve the image of the service.     

On joint railway and housing development strategy

Transit Oriented Development (TOD) with railway service is recognized as a sustainable mode of development for highly dense megacities. In addition to providing safe and efficient transit services, reducing auto dependence and therefore less need for highway expansions, the improved accessibility of TOD influences commuters’ residential location choices and the resultant housing value. Traditionally, statistical approaches have been used to estimate the relationship between railway development and housing value for individual sites. To some degree, TOD has also been studied with integrated land-use transport models. While useful, they lack an analytical framework to study the region-wide impacts of TOD on residential location and travel choices and the resultant land value changes. In this study, the joint railway and housing development strategy is modeled based on a combined equilibrium formulation with the bid-rent process. The problem is formulated as a mathematical program with equilibrium constraints, in which the upper level optimizes the objective for the joint development strategy by deciding on the combination of housing supplies and railway service levels. Analytical results are obtained for a single corridor in a multi-modal transport network, which are further illustrated by sensitivity analyses. A numerical example is constructed to demonstrate the approach and compare with other separate development strategies. The results generally confirm the synergy between railway and housing developments.     

Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.     

Non-discriminatory train dispatching in a rail transport market with multiple competing and collaborative train operating companies

Train dispatching is vital for the punctuality of train services, which is critical for a train operating company (TOC) to maintain its competitiveness. Due to the introduction of competition in the railway transport market, the issue of discrimination is attracting more and more attention. This paper focuses on delivering non-discriminatory train dispatching solutions while multiple TOCs are competing in a rail transport market, and investigating impacting factors of the inequity of train dispatching solutions. A mixed integer linear programming (MILP) model is first proposed, in which the inequity of competitors (i.e., trains and TOCs) is formalized by a set of constraints. In order to provide a more flexible framework, a model is further reformulated where the inequity of competitors is formalized as the maximum individual deviation of competitors’ delay cost from average delay cost in the objective function. Complex infrastructure capacity constraints are considered and modelled through a big M-based approach. The proposed models are solved by a standard MILP solver. A set of comprehensive experiments is conducted on a real-world dataset adapted from the Dutch railway network to test the efficiency, effectiveness, and applicability of the proposed models, as well as determine the trade-off between train delays and delay equity.     

Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence

Travel behaviour analysis has recently witnessed a rapidly growing interest in regret-based models of choice behaviour. Two different model specifications have been introduced in the transportation literature. Chorus et al. (Transportation Research B 42: 1–18, 2008a; in: Proceedings 87th Annual Meeting of the Transportation Research Board, Washington DC, 2008b) specified regret as a (non) linear function of the difference between the best-foregone choice alternative and the chosen alternative. Later, as an approximation to the original specification, Chorus (2010) suggested a logarithm function of utility differences between all choice alternatives, mainly for ease of estimation. This paper makes two contributions to this literature. First, formal analyses are conducted to identify the parameter space where the logarithmic specification becomes theoretically inferior to the original specification. Second, an empirical stated choice study on the choice of shopping centre is conducted to empirically test which specification best describes stated choices. Results suggest that for the collected data the original specification outperforms the new specification. Implications of this finding for the application of regret-based choice models in travel behaviour analysis are discussed.     

Itinerary choice and advance ticket booking for high-speed-railway network services

This paper formulates and examines the passenger flow assignment (itinerary choice) problem in high-speed railway (HSR) systems with multiple-class users and multiple-class seats, given the train schedules and time-varying travel demand. In particular, we take into account advance booking cost of travelers in the itinerary choice problem. Rather than a direct approach to model advance booking cost with an explicit cost function, we consider advance booking cost endogenously, which is determined as a part of the passenger choice equilibrium. We show that this equilibrium problem can be formulated as a linear programming (LP) model based on a three-dimension network representation of time, space, and seat class. At the equilibrium solution, a set of Lagrange multipliers for the LP model are obtained, which are associated with the rigid in-train passenger capacity constraints (limited numbers of seats). We found that the sum of the Lagrange multipliers along a path in the three-dimension network reflects the advance booking cost of tickets (due to advance/early booking to guarantee availability) perceived by the passengers. Numerical examples are presented to demonstrate and illustrate the proposed model for the passenger assignment problem.     

A non-compensatory choice model incorporating attribute cutoffs

This research proposes an extension to the traditional compensatory utility maximization framework which has guided most theoretical and statistical work in choice modeling applications, including those in transportation demand estimation work. Attribute cutoffs are incorporated into the decision problem formulation; it is then argued on extant empirical evidence that individuals may view these constraints as “soft”. This leads to the formulation of a penalized utility function that allows for constraint violation, but at a cost to the overall evaluation of the good. The proposed model is able to represent fully compensatory, conjunctive and disjunctive choice strategies, as well as combinations thereof. The properties of the proposed theoretical model are examined and discussed. From the theoretical framework, statistical models of choice behavior are easily derived; in their simplest forms, these models can be estimated using existing software. A Stated Preference choice experiment is analyzed using the proposed model, which is found to be highly consistent with observed choices and superior to a structural two-stage choice set formation model.     

Identifying preferences for public transport investments under a constrained budget

As urban areas face increasing demands for new transport infrastructure to promote a sustainable future with an increasing reality of constrained government budgets, the debate on whether we should focus on rail or bus-based investments continues unabated in many jurisdictions. Associated with the debate is an emotional (or ideological) bias by communities in favour of one mode, especially rail, which carries much sway at the political level as if there is no budget constraint. This paper presents a stated choice experiment to investigate this context as two unlabelled options described by 20 potential drivers of community preferences for improved public transport, where each choice scenario is conditioned on an estimated construction cost and a total annual transport infrastructure budget for the relevant geographical jurisdiction. This is followed by a labelling of each alternative to reveal whether the option is bus rapid transit (BRT) or light rail (LRT) and to establish whether this additional information influences preference revision. Data is collected in all eight capital cities of Australia in mid 2014. Mixed logit models with heteroscedastic conditioning in terms of the cost of the project infrastructure and whether the alternative is labelled BRT or LRT, provide new evidence on the nature and extent of community modal bias in a budget-constrained choice setting. The conclusions are twofold. On the one hand, if a fully compensatory choice rule is assumed (as is common in all previous modal comparison studies), LRT is predominantly preferred over BRT despite budgetary constraints, similarities in quality of service attributes and the opportunity to choose a greater network coverage for a given construction cost. However, when we allow for attribute non-attendance (a semi-compensatory choice rule), the modal bias is no longer a significant driver of preferences.     

Methodology for the qualitative evaluation of pedestrian crossings at road junctions with traffic lights

The aim of this work is to test the application of a method for making a qualitative evaluation of pedestrian crossings, based on the methodology of Khisty (Transportation Research Record 1438:45–50, 1994). The study identifies the Performance Measures (Comfort, Safety, System Continuity), with their respective attributes (waiting time, space available while waiting to cross, number of pedestrians, one-way or two-way street, state of the road surface, road width, vehicle speed, visibility, lighting conditions, guardrails, absence of obstacles in vicinity, state of sidewalks, lowered kerb, pedestrian signals, central island), which may be utilized in the evaluation. The first step was to ascertain the relative importance, from the point of view of the pedestrian, of the Performance Measures employed. Then the level-of-service (LOS), as perceived by the users, was determined for each of the pedestrian crossings in the survey, on the basis of the users’ level of satisfaction with each attribute. Khisty’s methodology makes it possible to relate the overall level of satisfaction with a qualitative LOS for the pedestrian facility under analysis. The chosen methodology was adapted to the Brazilian context, in a case study carried out in the city of São Paulo (Brazil), in collaboration with the local Traffic Engineering Corporation (Companhia de Engenharia de Tráfego, CET-SP). To this end, four pedestrian crossings at road junctions with traffic lights were analysed. The qualitative LOS obtained were compared to the quantitative LOS, calculated according to the Highway Capacity Manual (TRB 2000).     

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.     

Paratransit Service in Indonesia: User Satisfaction and Future Choice

Abstract

This paper explores user satisfaction with making use of paratransit in Bandung, Indonesia. User perceptions of attributes are analyzed using factor analysis to find representing attribute types and factors of service quality. Ordinal probit analysis is employed to explore the characteristics of users regarding their perception of satisfaction with the paratransit service. Binary logistic analysis is undertaken to predict users’ choice probability in making use of paratransit in the future when there is a service improvement. The study reveals that what may be of importance in developed countries carries a different weight of importance for paratransit users. This fact can be explained by describing the characteristics of users that expressed their satisfaction, which also explains the target market for this mode. The analysis reveals the notion that financial motives seem to dominate when users select to use paratransit.     

Estimating the willingness to pay and value of risk reduction for car occupants in the road environment

In recent years there has been a re-focus on the valuation of a statistical life from the ex post or human capital method to an ex ante willingness to pay (WTP) approach. This is in part a recognition that we may have been undervaluing the cost of fatalities and injuries to society associated with crashes, but also a strong belief in the need to focus on establishing the amount, ex ante, that individuals are willing to pay to reduce the risk of exposure to circumstances that might lead to death or degree of injury on the road network. This study has developed a framework in which to identify the degree of preference heterogeneity in willingness to pay by individuals who are drivers or passengers in cars to avoid being killed or injured. A stated choice experiment approach is developed. The empirical setting is a choice of route for a particular trip that a sample of individuals periodically undertakes in Australia. The particular trip is described in enough detail to provide the respondent with a familiar market environment, providing all the relevant background information required for making a decision. Mixed logit models are estimated to obtain the marginal (dis)utilities associated with each influence on the choice amongst the attribute packages offered in the stated choice scenarios. These estimates are used to obtain the WTP distributions for fatality and injury avoidance, which are then aggregated to obtain estimates of the value of risk reduction (VRR), of which the fatality class is also known as the value of a statistical life (VSL).     

Estimation of rail passenger flow and system utilization with ticket transaction and gate data

Capturing the dynamics in passenger flow and system utilization over time and space is extremely important for railway operators. Previous studies usually estimated passenger flow using automatic fare collection data, and their applications are limited to a single stopping pattern and/or a single type of ticket. However, the conventional railway in Taiwan provides four types of ticket and five types of train service with a number of stopping patterns. This study develops a comprehensive framework and corresponding algorithms to map passenger flow and evaluate system utilization. A multinomial logit model is constructed and incorporated in the algorithms to estimate passenger train selection behavior. Results from the empirical studies demonstrate that the developed framework and algorithms can successfully match passengers with train services. With this tool, operators can efficiently examine passenger flow and service utilization, thereby quickly adjusting their service strategies accordingly to improve system performance.     

Freight transport demand revisited: A microeconomic view of multimodal, multicharacteristic service uncertainty and the demand for freight transport

This paper provides a unifying framework for micro-based models of freight transport demand. An expected utility of profits model is posed that clarifies the conditions under which a system of equations (continuous) regression model should be used as opposed to the conditions that make a quantal choice model appropriate. The model also indicates the nature of subjective data that is necessary for estimating demand. In general, the conditions that give rise to the appropriate use of a quantal choice model imply that the needed subjective information is completely contained in the subjective probability distributions on the service characteristics; the utility function itself is irrelevant.     

A behavioural choice model for the evaluation of railway supply and pricing policies

This paper presents a choice model that can be used to simulate the impacts of variations in medium–long distance railway service characteristics (e.g. timetable, travel time, prices) on user choice of service type, run and class. The model, based on a nested-logit service/run/class/access–egress mode choice model, explicitly takes into account the desired departure (arrival) time and related early/late schedule delay penalties.     

Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach

In recent years we have seen an explosion of research seeking to understand the role that rules and heuristics might play in improving the predictive capability of discrete choice models, as well as delivering willingness to pay estimates for specific attributes that may (and often do) differ significantly from estimates based on a model specification that assumes all attributes are relevant. This paper adds to that literature in one important way—it explicitly recognises the endogeneity issues raised by typical attribute non-attendance treatments and conditions attribute parameters on underlying unobserved attribute importance ratings. We develop a hybrid model system involving attribute processing and outcome choice models in which latent variables are introduced as explanatory variables in both parts of the model, explaining the answers to attribute processing questions and explaining heterogeneity in marginal sensitivities in the choice model. The resulting empirical model explains how lower latent attribute importance leads to a higher probability of indicating that an attribute was ignored or that it was ranked as less important, as well as increasing the probability of a reduced value for the associated marginal utility coefficient in the choice model. The model does so by treating the answers to information processing questions as dependent rather than explanatory variables, hence avoiding potential risk of endogeneity bias and measurement error.     

Air carrier demand

Modeling air carrier demand is instrumental to understanding the relative importance of competitive forces that shape the airline environment and determine a carrier's market share. This paper develops a conceptual framework for analyzing carrier demand in a competitive context and applies that framework to study air carrier choice. This framework can be used by carriers to assess the market share and revenue implications of service design, pricing, marketing, and promotional strategies. We adopt an individual traveler choice approach to identify and measure the relative importance of factors which influence air travel demand. Travelers' patterns of air travel, perceptions of carrier service, frequent-flyer program membership, and carrier choice behavior are used to estimate models of individual carrier choice. These models indicate the importance of carrier presence in the origin market, carrier service in a city pair market (share of flights), carrier quality of service reflected in ratings by individual travelers, and traveler loyalty reflected in frequent-flyer program membership on carrier choice. The importance of these variables and the specific quantitative relationship estimated, can be used to estimate the market share impact of service design, pricing, marketing, and promotional changes. The empirical results of this study demonstrate the dramatic impact of frequent-flyer program participation on carrier choice for individual flights. These effects are particularly strong among the most important air carrier market, the frequent business traveler.     

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.     

A flexible gravity-opportunities model for trip distribution

This paper develops a flexible gravity-opportunities model for trip distribution in which standard forms of the gravity and opportunities models are obtained as special cases of a general opportunities (GO) model. Hence the question of choice between gravity or opportunities approaches is decided empirically and statistically by restrictions on parameters which control the global functional form of the trip distribution mechanism. The test for the gravity model is shown to be equivalent to a test of the IIA axiom where alternatives are destinations.The notational dichotomy between the two approaches is resolved by employing ordered trip matrices and transformations to permit row and column sum constraints to be applied. These constraints, often interpreted in various ways, are treated as normalisation terms and are therefore not strictly part of the form of the model. Doubly constrained, singly constrained and unconstrained versions of both models are developed throughout.A key step in the integration is the specification of an opportunity function which has as arguments destination-attribute variables such population, income or some other measure of opportunities and generalized cost/impedance-type variables relating origin and destination. This device obviates the mutual exclusiveness ordinarily required of these two sets of variables.The opportunity function is incorporated into a general proportionality factor which is defined by the difference in functions of cumulative opportunities; the latter are subjected to a convex combination of direct and inverse Box-Cox transformations. Different values of the parameters controlling these transformations generate contrasting families of models, notably the exponential and logarithmic intervening opportunities models and the gravity model. All models are shown to be embedded in a transformed triangular region over which likelihood function, response surface or simultaneous confidence interval contours may be plotted.These generalised gravity-opportunity concepts are applied to two well-known models: direct demand multimodal travel demand models, and the estimation of the OD matrix from link volumes. The second case is estimated empirically and here it is shown that a significant improvement is obtained over the gravity model, which is rejected, along with the logarithmic intervening opportunity model, in favour of a more general direct opportunities version.