scholarly journals Impact of Travel Time Constraints on Taste Heterogeneity and Non-Linearity in Simple Time–Cost Trade-Offs

2018 ◽  
Vol 2672 (49) ◽  
pp. 135-145
Author(s):  
Jeff Tjiong ◽  
Stephane Hess ◽  
Thijs Dekker ◽  
Manuel Ojeda Cabral
Keyword(s):  
Travel Time ◽  
Time Budget ◽  
Discrete Choice Models ◽  
Time Constraints ◽  
Preference Heterogeneity ◽  
Choice Data ◽  
Effective Measure ◽  
Trade Offs ◽  
Opt Out ◽  
Functional Forms

Discrete choice models are a key technique for estimating the value of travel time (VTT). Often, stated choice data are used in which respondents are presented with trade-offs between travel time and travel cost and possibly additional attributes. There is a clear possibility that some respondents experience time constraints, leaving some of the presented options unfeasible. A model not incorporating information on these constraints would explain choices for faster and more expensive options as an indication that those respondents have a higher VTT when in reality they may be forced to select the more expensive option as a result of their personal constraints. This paper puts forward the hypothesis that this can have major impacts on findings in terms of heterogeneity in VTT measures. This paper examines via simulation the bias in VTT estimates and especially preference heterogeneity when such constraints are (not) accounted for. Empirical evidence is provided that preference heterogeneity is confounded with the travel budget impact on the availabilities of alternatives, and it is shown that there is a risk of producing biased estimates for appraisal VTT if studies do not explicitly model choice set formation. The inclusion of an opt-out alternative could be an effective measure to reduce the bias. This paper also explores the potential use of non-linear functional forms to capture the time budget impacts.

Stochastic User Equilibrium Model with Implicit Travel Time Budget Constraint

2008 ◽  
Vol 2085 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Tony E. Smith ◽  
Chao-Che Hsu ◽  
Yueh-Ling Hsu
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Time Budget ◽  
User Equilibrium ◽  
Time Constraints ◽  
Stochastic User Equilibrium ◽  
Trade Offs ◽  
Travel Time Budget ◽  
Travel Decisions ◽  
The Impact

Although time constraints on travel behavior have been widely recognized, little effort has been made to incorporate such constraints into the traditional stochastic user equilibrium (SUE) framework. The major objective of this research is to fill this gap by incorporating travel time constraints into the SUE model by means of a nonlinear perceived travel time function. This modified model, designated the travel time budget model, focuses primarily on discretionary travel behavior (such as shopping trips) and hence also allows the possibility of deferring travel decisions by incorporating an additional choice alternative designated the shop-less-frequently alternative. This model is compared with the traditional SUE model by using a simulated travel scenario on a test network designed to reflect a practical planning situation. The simulation shows that when attractiveness levels are increased by the introduction of a new shopping opportunity, the presence of travel time constraints can lead to significantly smaller predicted travel volumes than those of the traditional SUE model. More important, it shows that the overall pattern of travel can be quite different. In particular, travel to the shopping destination with enhanced attractiveness can actually decrease for some origin locations. The findings suggest that when an attempt is made to evaluate the impact of planning alternatives on future traffic patterns, it is vital to consider not only the cost of time itself but also the time trade-offs between travel and other human activities.

scholarly journals An artificial neural network based method to uncover the value-of-travel-time distribution

2020 ◽  
Author(s):  
Sander van Cranenburgh ◽  
Marco Kouwenhoven
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Travel Time ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Monte Carlo Data ◽  
Travel Time Distribution ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and taking the panel nature of stated choice data into account. To assess how well the proposed ANN-based method works in terms of being able to recover the VTT distribution, we first conduct a series of Monte Carlo experiments. After having demonstrated that the method works on Monte Carlo data, we apply the method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the promising results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

scholarly journals Airline choice model for an international round-trip flight considering outbound and return flight schedules

2020 ◽  
Vol 54 (2) ◽  
pp. 75-93 ◽  
Author(s):  
Claudia Munoz ◽  
Henry Laniado ◽  
Jorge Córdoba
Keyword(s):  
Travel Time ◽  
Choice Model ◽  
Stated Preference ◽  
Mixed Logit ◽  
Choice Data ◽  
Round Trip ◽  
Flight Schedules ◽  
Trade Offs ◽  
International Round ◽  
The Impact

This paper quantified the impact of outbound and return flight schedule preferences on airline choice for international trips. Several studies have used airline choice data to identify preferences and trade-offs of different air carrier service attributes, such as travel time, fare and flight schedule. However, estimation of the effect return flight schedules have on airline choice for an international round-trip flight has not yet been studied in detail. Therefore, this study introduces attributes related to return flight characteristics and round-trip flight schedule interaction into the airline choice models, which have not previously been reported in the literature. We developed a stated preference survey that includes round-trip fares based on flight schedule combinations and the number of days prior to departure fares was purchased. We applied modelling techniques using a set of stated preference data. A mixed logit model was tested for the presence of heterogeneity in passengers' preferences. Our results indicated that models with attributes related to return flight and its interaction with outbound flight attributes have a superior fit compared with models only based on attributes reported in the literature review. The model found shows that airfare, travel time, arrival preference schedule in the outward journey, departure preference in the return journey and the schedule combination of round-trip flight are significantly affecting passenger choice behaviour in international round-trip flights. Sensitivity analysis of airline service characteristics and their marketing implications are conducted. The analysis reports seven policies with the greatest impact on each airline choice probabilities. It shows that by reducing travel time and airfare and by adopting an afternoon and night schedule preference for outbound and return flight, respectively, the highest probability on airline choice would be reached. This research contributes to the current literature by enhancing the understanding of how passengers choose airlines, considering both outbound and inbound journey characteristics. Thus, this study provides an analytical tool designed to provide a better understanding of international round-trip flight demand determinants and support carrier decisions.

scholarly journals Values of Travel Time for Recreational Trips under Different Behavioural Rules

2021 ◽  
Vol 13 (12) ◽  
pp. 6831
Author(s):  
Rosa Marina González ◽  
Concepción Román ◽  
Ángel Simón Marrero
Keyword(s):  
Travel Time ◽  
National Parks ◽  
Traffic Congestion ◽  
National Park ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Model Specification ◽  
Regret Minimization ◽  
Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

scholarly journals Travel-Time Constraints on Destination-Choice Sets

2010 ◽  
Vol 29 (2) ◽  
pp. 108-123 ◽  
Author(s):  
Jean-Claude Thill ◽  
Joel L. Horowitz
Keyword(s):  
Travel Time ◽  
Destination Choice ◽  
Time Constraints ◽  
Choice Sets

The effects of personal experience on choice-based preferences for wildfire protection programs

2013 ◽  
Vol 22 (2) ◽  
pp. 234 ◽  
Author(s):  
Thomas P. Holmes ◽  
Armando González-Cabán ◽  
John Loomis ◽  
José Sánchez
Keyword(s):  
Willingness To Pay ◽  
Personal Experience ◽  
Choice Experiment ◽  
Prior Experience ◽  
Preference Heterogeneity ◽  
Risk Averse ◽  
Risk Seeking ◽  
Economic Rationale ◽  
Trade Offs ◽  
Risk Loss

In this paper, we investigate homeowner preferences and willingness to pay for wildfire protection programs using a choice experiment with three attributes: risk, loss and cost. Preference heterogeneity among survey respondents was examined using three econometric models and risk preferences were evaluated by comparing willingness to pay for wildfire protection programs against expected monetary losses. The results showed that while nearly all respondents had risk seeking preferences, a small segment of respondents were risk neutral or risk averse. Only respondents who had personal experience with the effects of wildfire consistently made trade-offs among risk, loss and cost and these respondents were willing to pay more for wildfire protection programs than were respondents without prior experience of the effects of wildfire. The degree to which people with prior experience with the effects of wildfire can effectively articulate an economic rationale for investing in wildfire protection to other members of their own or other communities facing the threat of wildfires may influence the overall success of wildfire protection programs.

scholarly journals A DISCRETE CHOICE MODEL FOR FIRM LOCATION DECISION

2016 ◽  
Vol 14 (4) ◽  
Author(s):  
Noordini Che Man ◽  
Harry Timmerman
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Central Business District ◽  
Statistical Technique ◽  
Discrete Choice Models ◽  
Location Decisions ◽  
Firm Location ◽  
Suitability Analysis ◽  
Choice Data ◽  
Business District

Where to locate? It is one of the most important question in locating a business in a city. In the city center, business or firms are functioning as a dominant attractor of employment and also employment locations which linked the land use and transportation system. The objective of this paper is to describe the location model of firms in Kuala Lumpur area. Two important determinants of location choice model in this study are the accessibility measures and the suitability analysis indicators. The model focuses on the statistical technique for analyzing discrete choice data by using econometric and Geographic Information System software. The findings in this paper show that agriculture, mining, electricity, gas and water, transport and finance firms' type are mostly located outside of Kuala Lumpur's Central Business District area. Meanwhile, manufacturing, construction and wholesale firms' type are located in the Central Business District area. The result of this study will highlight the use of discrete choice models in the analysis of firm location decisions which will be a foundation to facilitate town planners and decision makers to understand the firm location decisions in their region.

scholarly journals Discrete Choice Data with Unobserved Heterogeneity: A Conditional Binary Quantile Model

2019 ◽  
Vol 28 (2) ◽  
pp. 147-167
Author(s):  
Xiao Lu
Keyword(s):  
Discrete Choice ◽  
Unobserved Heterogeneity ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Science Data ◽  
Explanatory Variables ◽  
Conditional Mean ◽  
Heterogeneous Effects ◽  
Discrete Response ◽  
Legislative Proposals

In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.

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