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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>On effects of behavioral intention on travel preferences for electric car-sharing services: Empirical insights from the Netherlands</title>
      <link>https://trid.trb.org/View/2478300</link>
      <description><![CDATA[Behavioral intention and use behavior toward emerging mobility services have received much attention recently in transportation research. Although it is commonly conceptualized that behavioral intention affects use behavior, these two subjects have been studied largely in isolation. To fill this gap, this paper investigates the effects of behavioral intention on travel preferences for electric car-sharing services (ECS). Based on a stated choice experiment with measurements of psychological factors deployed in the Netherlands, a series of hybrid choice models were established, in which various effects of behavioral intention on travel preferences were examined. The estimation of these models reveals the following findings. First, behavioral intention significantly influences people’s preferences for ECS. Holding all else unchanged, a higher behavioral intention results in a higher propensity to choose ECS. Second, people’s socio-demographic characteristics and travel context have significant interaction effects with behavioral intention and further influence their preferences for ECS. Third, among the ECS-specific attributes, access time has a significant interaction effect with behavioral intention, which can decrease the value of access time. Fourth, compared to social influence and personal attitude, peoples’ behavioral intention toward ECS has a larger influence on their choices.]]></description>
      <pubDate>Mon, 30 Dec 2024 09:57:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2478300</guid>
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    <item>
      <title>Passenger valuation of interchanges in urban public transport</title>
      <link>https://trid.trb.org/View/2371206</link>
      <description><![CDATA[Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study the authors derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose they estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. They find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, their model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Their study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.]]></description>
      <pubDate>Tue, 30 Apr 2024 15:18:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2371206</guid>
    </item>
    <item>
      <title>Group Level Agent-Based Mixed Logit for Nonparametric Estimation of K-Modal Taste Heterogeneity with a Ubiquitous Data Set</title>
      <link>https://trid.trb.org/View/2334489</link>
      <description><![CDATA[Estimating agent-specific taste heterogeneity with a large sample size requires both model flexibility and computational efficiency. The authors propose a group-level agent-based mixed (GLAM) logit approach that is estimated with inverse optimization (IO) and group-level market share. The model is theoretically consistent with the RUM model framework, while the estimation method is a nonparametric approach that fits to market-level datasets, which overcomes the limitations of existing approaches. A case study of New York statewide travel mode choice is conducted with a synthetic population dataset provided by Replica Inc., which contains mode choices of 19.53 million residents on two typical weekdays, one in Fall 2019 and another in Fall 2021. Individual mode choices are grouped into market-level market shares per census block-group OD pair and four population segments, resulting in 120,740 group-level agents. The authors calibrate the GLAM logit model with the 2019 dataset and compare to several benchmark models:  mixed logit (MXL), conditional mixed logit (CMXL), and individual parameter logit (IPL). The results show that empirical taste distribution estimated by GLAM logit can be either unimodal or multimodal, which is infeasible for MXL/CMXL and hard to fulfill in IPL. The GLAM logit model outperforms benchmark models on the 2021 dataset, improving the overall accuracy from 82.35% to 89.04% and improving the pseudo R-square from 0.4165 to 0.5788. Moreover, the value-of-time (VOT) retrieved from GLAM logit aligns with the empirical knowledge (e.g., VOT of NotLowIncome population in NYC is $28.05/hour). The agent-specific taste parameters are essential for the policymaking of statewide transportation projects.]]></description>
      <pubDate>Tue, 20 Feb 2024 09:16:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2334489</guid>
    </item>
    <item>
      <title>Stated Preferences on Shared Automated Vehicles in the Context of Mode Choice Model Estimation for Different Trip Purposes: A Case Study for Flanders, Belgium</title>
      <link>https://trid.trb.org/View/2317346</link>
      <description><![CDATA[Vehicle automation and shared and on-demand mobility are recognized as a technology and concept that, combined, can bring significant opportunities for our mobility system. However, when deployed improperly, they could entail risks as well. Regulation will be necessary to steer the deployment of this technology into a sustainable direction. Therefore, it is necessary to gain knowledge of potential user-profiles and insights into mode choice behavior that could guide policy making. This study examines the value that travelers place on various aspects of a shared automated service, as well as the ways that shared automated vehicle services might replace the conventional modes of transportation such as private vehicles, public transportation, and bicycles. For this purpose, an online stated preference survey was conducted in Flanders, Belgium. To this end, the authors collected a sample of 652 completed questionnaires. The results show that although SAV is defined as a station-based service in this paper, it exhibits the best substitutional pattern with the car compared to the other modes. Moreover, the authors concluded its value of travel time is the highest among all modes after private car, and it changes across different trip purposes.]]></description>
      <pubDate>Sun, 07 Jan 2024 12:57:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2317346</guid>
    </item>
    <item>
      <title>Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity</title>
      <link>https://trid.trb.org/View/2050472</link>
      <description><![CDATA[The major challenges in dynamic activity-based models include predicting activity-related choices and understanding inherent heterogeneous preferences. The dynamic discrete choice model (DDCM) has been used for daily activity-travel planning. However, ignoring unobservable heterogeneity can bias the estimation and prediction results. To solve this problem, the authors propose a DDCM that accounts for unobserved heterogeneity to capture useful hidden information on travelers’ characteristics. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. The algorithm iteration depends mainly on the mapping relationship between posterior distributions and conditional choice probabilities. Meanwhile, a less complex log-likelihood function is proposed in the maximization step to estimate two types of parameters simultaneously. The proposed techniques are verified using household travel survey data from Chongqing (China). Two unobserved types of travelers, time and cost sensitivity, are identified based on the Bayesian information criterion (BIC) value. Time- and segment-varying sensitivity analyses are conducted to present choice probability differences of mode and activity scheduling under a one-unit increase in the number of schoolchildren and cars. Different impacts on activity-travel patterns, such as trip frequency, mode share, and activity schedule, generated by changes in auto ownership, are analyzed. Finally, the adjusted rho-squared value, BIC values compared with other single-effect models, and aggregate validation results demonstrate the exceptional performance of the proposed model.]]></description>
      <pubDate>Tue, 24 Jan 2023 09:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2050472</guid>
    </item>
    <item>
      <title>Accounting for Heterogeneity of Travel Time Reliability Valuation in Shippers’ Freight Mode Choice</title>
      <link>https://trid.trb.org/View/1925994</link>
      <description><![CDATA[Designing efficient strategies to adjust freight transportation mode structure requires in-depth understanding of shippers’ mode choice behavior. This paper presents an empirical study to investigate preference heterogeneity in value of reliability (VOR) for hinterland leg transportation mode choice. A stated preference survey using a D-efficient design approach is carried out in the corridor from Yiwu to the port of Ningbo to collect data on shippers’ behavior. Two model specifications including the Base Model and the Heterogeneous Model are developed to analyze these data. Mixed logit is applied to estimate the parameters of models. The estimation results of the base model reveal significant preference heterogeneity in shippers’ VOR. We then calculate the mean and variation of overall VOR. In addition, the potential factors leading to heterogeneity in VOR are identified. Results imply that commodity characteristics including shipment size, value, and weight could partially explain the shippers’ heterogeneity in VOR. Based on these factors, eight sub-groups of container shippers are obtained, and the mode shares of railway under different levels of railway reliability are estimated for each sub-group. Results show that improvement in the level of reliability is important to increase the mode share of rail, especially in the sub-group where shipments are light and of high value. The findings of this paper can be used for demand forecasting and transportation policy making.]]></description>
      <pubDate>Mon, 14 Mar 2022 16:24:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1925994</guid>
    </item>
    <item>
      <title>The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service</title>
      <link>https://trid.trb.org/View/1880777</link>
      <description><![CDATA[The growing popularity of Mobility as a Service (MaaS) has led researchers to realize that it can be used for the optimal management of the transportation system of a city. This study is to solve the whole day multimodal path planning problem considering user-specific modal preference, which can be used on the recommended system of the MaaS platform. The mathematical formulations of the whole day multimodal shortest hyperpath problem are proposed while accounting for individual constraints, such as the time window, multicriteria, and park-and-ride demands, to develop customized travel schemes. The primary obstacle involves consideration of the travelers’ modal preferences to develop an optimal whole day journey. The dynamic discrete choice model (DDCM) that accounts for unobservable heterogeneity is proposed to characterize dynamic mode choice behavior and generate a set of feasible mode chains. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. In this way, the posterior probability can be adapted to the conditional choice probability estimation. The label correcting concept is used to develop a user-constrained shortest hyperpath algorithm that can solve the multimodal shortest hyperpath problem and calculate the Pareto set of the whole day travel schemes according to the feasible mode chains and different constraints. The proposed techniques are verified by utilizing household travel survey data and multimodal network data in Nanjing (China). Two unobserved states of travelers, namely time- and cost-sensitive, are identified based on the BIC value. The adjusted rho-squared value, accuracies of predicted mode chains, along with the aggregate validation results, confirm the model’s effectiveness. The top two resulting hyperpaths are generated for a randomly chosen traveler on the basis of his or her preferences. This study is an important step toward promoting MaaS and improving the sales of MaaS bundles.]]></description>
      <pubDate>Sun, 31 Oct 2021 19:07:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1880777</guid>
    </item>
    <item>
      <title>A New Travel Demand Model for Outdoor Recreation Trips</title>
      <link>https://trid.trb.org/View/1728828</link>
      <description><![CDATA[This paper is based on the dissertation of the authors PhD research in the Martin Centre for urban and transport studies at the University of Cambridge. It reports on development of a new travel demand model, that is capable of representing and predicting travel to individual outdoor recreational sites. Travel to outdoor recreational spaces belongs to a general class of research questions for understanding destination and travel mode choices. The relevant literature suggests that the analysis and modelling of outdoor recreational trips appear to fall between two poles: on the one hand, the Discrete Choice Model (DCM) based travel modelling, has rarely touched outdoor recreational travel; on the other hand, the regression models that have been developed and applied within the environmental geography and economics for predicting outdoor recreational travel have not engaged in the field of travel demand modelling; instead, such models place a unique emphasis on the use of land cover/land use data which is absent from the four-step models. Therefore, outdoor recreational travel demand remains poorly understood in transportation modelling. Why understood travel to outdoor recreational sites is important? Besides the age-old belief that outdoor activities are good for the body and spirit, in recent years, there has been a growing evidence base showing that outdoor recreation is closely associated with human health and wellbeing. This evidence appears to have started to influence how people perceive the benefits of outdoor recreation. In a systematic survey on Monitor the Engagement with the Natural Environment (MENE) that has been going for nine years, the proportion of outdoor recreation visits where health and exercise were cited as a motivation rose from 34 per cent in 2009 to 50 per cent in 2018. The overall number of outdoor recreational trips has risen from 2.8 billion (2009-2010) to 3.4 billion (2017-2018). Given the importance of outdoor recreational activities to urban land use planning and public health, this is a clear gap needs to be filled. Since the MENE survey came online in 2010, it becomes possible to study outdoor recreational trips without extensive on-site data collecting exercise which is time-consuming and expensive. The UK National Ecosystem Assessment (NEA) has developed a Negative Binomial Regression (NBR) model to estimate the outdoor recreational trips generation and distribution. Although it has proven the value of this kind of model for city planners and designers by testing different planning scenarios, the model is not intended for assessing choices of individual sites. One reason for this, as identified by previous studies, is that compared with the DCMs, the NBR models have certain limits on estimating people’s choice behaviours. In this study a new DCM based travel demand model has been developed for outdoor recreational trips. This is achieved by answering three main research questions: First, how to build a new model for outdoor recreational travel? Secondly, is the estimation accurate enough? And, finally, how can city planners and designers use this new method? The data used to build up the new model are collated from a wide range of sources, most of which are open-source data includes: OpenStreetMap, Ordnance Survey map, Google API, literature reviews and the most importantly, the MENE survey data. The new model has been calibrated for a case study area which spanned 14 selected districts in the North-West region. Validation of the new model is based on counting data of a recreational site - Wigg Island Nature Reserve. In the final stage of the research, the new model is applied to estimate the changes that would arise from planning and design interventions in existing and proposed sites. The outcome of this study is a new model that can predict the number of trips to individual destinations, and that the model presented here is capable of predicting the changes in the volume and catchment of visits to an existing green space after land use planning or urban ecological interventions. This is a completely new theoretical model that is focused on understanding and quantifying the travel choices to outdoor recreation sites, which can be transferred to any other sites in England with little requests for data collection.]]></description>
      <pubDate>Thu, 27 Aug 2020 11:08:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1728828</guid>
    </item>
    <item>
      <title>Analysis of Travel-Time Use in Crowded Trains using Discrete-Continuous Choices of Commuters in Tokyo, Japan</title>
      <link>https://trid.trb.org/View/1723811</link>
      <description><![CDATA[Travel-based multitasking and the possibility to perform activities during travel are important factors that can make a transportation mode attractive. However, serious crowding in public transportation systems might adversely affect the passengers’ free choice to participate in activities during travel. This study aims to examine how crowding in public transportation systems is related to discrete-continuous choices in different types of multitasking options using a data set of 500 commuters in the Tokyo Metropolitan Area. Employing a multiple discrete-continuous extreme value model, this study investigates the relationship between crowding levels and multitasking behavior. The results show that high crowding levels, relative to being seated, have a significantly negative association with many multitasking options, which suggests the importance of seat availability. The estimation results also show that information and communication technology (ICT)-dependent leisure activities and non-ICT active activities, such as reading and talking with other passengers, have the lowest satiation and higher baseline preference constants, which indicates that they are preferred by passengers. Meanwhile, crowding levels were observed to have a significant relationship with these multitasking activities. Finally, the key findings, contributions, and policy implications of the findings are discussed.]]></description>
      <pubDate>Tue, 28 Jul 2020 16:30:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1723811</guid>
    </item>
    <item>
      <title>Mode choice with latent availability and consideration: Theory and a case study</title>
      <link>https://trid.trb.org/View/1654948</link>
      <description><![CDATA[Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. The authors' findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.]]></description>
      <pubDate>Mon, 28 Oct 2019 10:27:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1654948</guid>
    </item>
    <item>
      <title>Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns</title>
      <link>https://trid.trb.org/View/1628052</link>
      <description><![CDATA[Shared automated vehicles have the potential to revolutionize future transportation mode choice. Because shared automated vehicles could be a disruptive transportation modal alternative, understanding the factors that may affect the likelihood of using and possible concerns is extremely important. To do so, the current paper uses a survey of American Automobile Association members to ask whether or not survey respondents were willing to use shared automated vehicles if they became available. They were also asked their main concerns associated with this technology (safety, privacy, reliability, travel time or travel cost). Two random parameter logit models were estimated to gain insights into the likely usage/concerns processes. Some of the key variables playing statistically significant roles in the willingness to use of shared automated vehicles were ethnicity, household size, daily travel times, and vehicle crash history. Respondents from one vehicle households, that were in close proximity to grocery stores, and have previously been involved in a vehicle crash, were found to be more willing to use shared automated vehicles. Other variables significant in the analysis were high education indicator and driving alone for commute indicator. With regard to shared automated vehicle concerns, the characteristics of respondents who were more or less likely to be concerned with safety, reliability, privacy, and travel time/travel cost were identified. While the opinions and perceptions towards shared automated vehicles are likely to fluctuate in the coming years as more and more information relating to the potential of such sharing becomes available, the findings provide an important initial assessment before this technology becomes widely available to the public. The more is known about shared automated vehicles and their early adopters, the better and seamless the potential modal transition can be. Learning what groups of people are more or less willing to use this technology will help to improve the overall mobility of all. Combining the significant variables provides a rough profile description of early users of shared automated vehicles and their environment. This helps to prioritize possible investments and allows the policy and auto makers to identify the critical needs of the users. This initial assessment provides the characteristics of early adopters and their travel behavior. The model estimation results clearly show that different socio-demographic groups value different aspects and have different concerns relating to shared automated vehicles.]]></description>
      <pubDate>Mon, 01 Jul 2019 09:20:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1628052</guid>
    </item>
    <item>
      <title>Analysis of dynamic decision-making in a bicycle-sharing auction using a dynamic discrete choice model</title>
      <link>https://trid.trb.org/View/1596248</link>
      <description><![CDATA[For clarifying the usefulness and practical issues of a tradable permit system empirically, we implemented a tradable permit system for a bicycle-sharing service in Yokohama city, Japan. We analyzed both travel and transaction behavior within this system. Many activity factors, such as the amount of free time in each day, home location and travel mode to the bicycle port, were shown to affect the transaction of tradable permits. The results of the pilot program indicated that inefficient allocation of tradable permits occurred when participants postponed their decision-making because of uncertainty. To determine the reason for this effect and the contributing factors, we created a dynamic discrete choice model to describe the choice results and timing. The estimation result indicated that the option value of postponing decision-making caused the transactions to be performed at the last minute, and that this effect blocked the liquidity of the permits trade. In addition, because the result reveals that there was heterogeneity in the time discount factor, the initial allocation of permits was found to be important for efficient allocation.]]></description>
      <pubDate>Tue, 30 Apr 2019 09:21:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1596248</guid>
    </item>
    <item>
      <title>Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions</title>
      <link>https://trid.trb.org/View/1489779</link>
      <description><![CDATA[This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation.]]></description>
      <pubDate>Tue, 02 Jan 2018 10:38:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1489779</guid>
    </item>
    <item>
      <title>Mode Shift Behavior of Commuters Due to the Introduction of New Rail Transit Mode</title>
      <link>https://trid.trb.org/View/1470701</link>
      <description><![CDATA[The cities of developing countries are undertaking implementation of rail based transit systems, especially metro rail, as a solution to the problems of urban traffic congestion and rapidly increasing travel demand, keeping in view the goal of sustainable development. Traffic congestion mainly finds its roots in the ever increasing number of private vehicles, which in turn is primarily attributed to low levels of service provided by existing public transport. After the introduction of metro system, however, an analysis as to whether the system has been successful in veering the masses away from their private modes and onto the new metro mode is of crucial importance. This paper presents such an investigation on the mode shifting behavior towards a newly operational metro rail mode in Mumbai city, India, so as to understand the key tangibles perceived most important by the end user responsible for the mode shift.  The investigation involves designing, implementing and analyzing Revealed and Stated Preference questionnaire surveys. The Revealed Preference (RP) survey has been conducted on commuters using the newly operational metro rail line which has provided the much needed east-west connectivity in Mumbai city, whereas an appropriately designed Stated Preference (SP) experiment has been administered on the commuters living within the catchment of a proposed additional metro rail line. The RP and SP datasets collected are used for estimating econometric mode choice models by analyzing the combined dataset using the sequential estimation method. Separate models are estimated for private vehicle users and public transit users. Before the development of a combined RP and SP model of mode choice, a discrete choice model using only the RP data explaining the mode shift behavior of commuters of new metro rail is developed and presented.  The RP survey shows approximately 80% of respondents were using public transport even before shifting to the new metro line, whereas approximately 60% of the respondents from SP survey who are currently using private vehicle show willingness to shift to the proposed metro rail line, showing the need for the metro rail alignment along the corridor. Results of this analysis have been used in updating parameters of existing transportation planning model and estimation of ridership on the proposed transit system along the new corridor, and realistically determining the value commuters assign to travel time savings and comfort inside transit vehicles.]]></description>
      <pubDate>Wed, 28 Jun 2017 14:39:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1470701</guid>
    </item>
    <item>
      <title>Modeling Consideration in Mode Choice: Application on Rome-Milan Corridor, Italy</title>
      <link>https://trid.trb.org/View/1438884</link>
      <description><![CDATA[This paper contributes to the discussion on the role of consideration sets in the estimation of mode choice models. Systematic misspecification of individuals’ choice sets (due to the inclusion of infeasible or unconsidered alternatives) might lead to biased parameters’ estimates, even when the universal set contains very few alternatives such as in mode choice decisions. For this study, a sample of travelers on the Rome-Milan corridor in Italy were presented with a stated choice survey in the form of an online journey planner where all alternatives were available for comparison. Answers to supplementary questions on awareness (through unaided recall), consideration at the task-level, and the presence of attributes’ thresholds have been linked to respondents’ socio-economic characteristics and then used in the estimation of a two-stage class allocation model accounting for individual specific consideration sets. Two specifications of this model are proposed, depending on whether consideration is assumed to depend on the values taken by the alternatives with respect to travel time or travel cost. Estimation results show significant improvements in model fit relative to a simpler multinomial logit model (which assumes instead consideration for all available alternatives), and differences in the estimated value of travel time hence providing further evidence that fully compensatory models may provide inferior results in terms of statistical fit in the presence of non-compensatory behaviors.]]></description>
      <pubDate>Mon, 24 Apr 2017 09:31:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1438884</guid>
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