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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Choice probabilities and correlations in closed-form route choice models: specifications and drawbacks</title>
      <link>https://trid.trb.org/View/2201293</link>
      <description><![CDATA[This paper investigates the performance – in terms of choice probabilities and correlations – of existing and new specifications of closed-form route choice models with flexible correlation patterns, namely the Link Nested Logit (LNL), the Paired Combinatorial Logit (PCL) and the more recent Combination of Nested Logit (CoNL) models. Following a consolidated track in the literature, choice probabilities and correlations of the Multinomial Probit (MNP) model by [1] are taken as targets. Laboratory experiments on small/medium-size networks are illustrated, also leveraging a procedure for practical calculation of correlations of any GEV models, proposed by [2]. Results show that models with inherent limitations in the coverage of the domain of feasible correlations yield unsatisfactory performance, whilst the specifications of the CoNL proposed in the paper appear the best in fitting both MNP correlations and probabilities. The performance of the models is appreciably ameliorated by introducing lower bounds to the nesting parameters. Overall, the paper provides guides for the practical application of tested models.]]></description>
      <pubDate>Thu, 27 Jul 2023 16:54:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2201293</guid>
    </item>
    <item>
      <title>A multinomial probit model with Choquet integral and attribute cut-offs</title>
      <link>https://trid.trb.org/View/1923192</link>
      <description><![CDATA[Several non-linear functions and machine learning methods have been developed for flexible specification of the systematic utility in discrete choice models. However, they lack interpretability, do not ensure monotonicity conditions, and restrict substitution patterns. The authors address the first two challenges by modeling the systematic utility using the Choquet Integral (CI) function and the last one by embedding CI into the multinomial probit (MNP) choice probability kernel. The authors also extend the MNP-CI model to account for attribute cut-offs that enable a modeler to approximately mimic the semi-compensatory behavior using the traditional choice experiment data. The MNP-CI model is estimated using a constrained maximum likelihood approach, and its statistical properties are validated through a comprehensive Monte Carlo study. The CI-based choice model is empirically advantageous as it captures interaction effects while maintaining monotonicity. It also provides information on the complementarity between pairs of attributes coupled with their importance ranking as a by-product of the estimation. These insights could potentially assist policymakers in making policies to improve the preference level for an alternative. These advantages of the MNP-CI model with attribute cut-offs are illustrated in an empirical application to understand New Yorkers’ preferences towards mobility-on-demand services.]]></description>
      <pubDate>Thu, 24 Mar 2022 17:26:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1923192</guid>
    </item>
    <item>
      <title>Developing an Accident Severity Model Based on Related Crash Type: Comparison of Four Commonly Used Discrete Choice Models</title>
      <link>https://trid.trb.org/View/1759909</link>
      <description><![CDATA[The purpose of the present study is to identify the risk factors responsible for particular crash types likely to result in more severe accidents, and to subsequently propose new strategies or countermeasures to lessen the probability of these kinds of crashes. What makes this study different from previous research is that this research can distinguish between two different crash types with the same type of collision. The results do not endorse the abilities of the traditional approaches, in which the type and severity of the crash were examined separately. Accordingly, the modeling process and results of this study seem to be quite beneficial in providing more effective preventive policies and countermeasures. In addition, this study seeks to compare the capability of different discrete choice techniques to find out which one performs better according to available dataset and study’s limitations. To address this goal, the present study employed different types of discrete choice models including multinomial logit (MNL), nested multinomial logit (NMNL), mixed logit (ML), and multinomial probit (MNP) to evaluate this indisputable correlations using R statistical software. In comparison with other discrete choice  techniques, ML demonstrated the best fit with the available dataset. Furthermore, since the framework of MLs are straightforward, and simulation of their choice probabilities was computationally simple.]]></description>
      <pubDate>Thu, 04 Feb 2021 11:00:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1759909</guid>
    </item>
    <item>
      <title>Bayes Estimation of Latent Class Mixed Multinomial Probit Models</title>
      <link>https://trid.trb.org/View/1759753</link>
      <description><![CDATA[Discrete choice models lie at the heart of many transportation models. This holds true for mode choice models as well as for models of vehicle purchase decisions, to name just a few applications. The standard random utility models often fail to take into account heterogeneity of individual deciders, compare for example Train (1), Chapter 6, or Train (2). Individual heterogeneity in preferences for different deciders has been modelled by imposing mixing distributions on the coefficients. However, the literature does not provide much guidance so far for the specification of the mixing distributions apart from trial and error procedures using several standard parametric distributions. Based on the ideas of Train (2) and Bhat and Lavieri (3) recently Bauer et al. (4) introduced procedures for non-parametrically estimating mixed multinomial probit models. While these procedures have been demonstrated to be useful in cross-sectional context, the evaluation of the probit likelihood in the panel setting proofs numerically challenging. Extending Scaccia and Marcucci (5) the authors propose an approach that on the one hand is capable of approximating the underlying mixing distribution while on the other hand being numerically favorable since no likelihood function has to be evaluated. The approach uses a Bayesian framework for estimating a latent class mixed multinomial probit model where the number of latent classes is updated within the algorithm on a weight-based strategy. Presenting simulation results, the authors demonstrate that the approach is suitable for guiding the specification of mixing distributions in empirical applications.]]></description>
      <pubDate>Thu, 04 Feb 2021 11:00:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1759753</guid>
    </item>
    <item>
      <title>Profiling commuters’ travel behavior in the Pacific states of the continental U.S.</title>
      <link>https://trid.trb.org/View/1739790</link>
      <description><![CDATA[The authors develop a more nuanced understanding of commuters’ travel mode choices by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental US. These profiles are created by utilizing the US Census Public Use Microdata Sample (PUMS) data. The microdata sample set allows for the estimation of fine-grained models that showcase how individual commuters make travel mode choices. The authors' results show appreciable locational variation in mode choices and statistically significant differences in commuting profile across and within population segments. A key revealing finding demonstrates that across the three states analyzed, the total number of vehicles driven for any day of the week could be reduced by up to 10 million assuming the commuting patterns observed in San Francisco applies to the rest of the states. The authors conclude with insights and policy implications provided by the study on making transportation-related infrastructure decisions.]]></description>
      <pubDate>Tue, 27 Oct 2020 12:23:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1739790</guid>
    </item>
    <item>
      <title>CoNL route choice model: numerical assessment on a real dataset of trajectories</title>
      <link>https://trid.trb.org/View/1709071</link>
      <description><![CDATA[This paper proposes a numerical assessment of the performances of the Combination of Nested Logits (CoNL) route choice model on a big-size network (Regione Campania). The CoNL is a particular specification of the Combination of Random Utility Models (CoRUM) model. Currently, its performances in terms of choice probabilities have been investigated only on some toy networks, by comparing route choice probabilities with reference to target Multinomial Probit choice probabilities. The paper provides also a discussion about different aspects of the CoNL for route choice: the possibility to take into account also non-efficient path, the computation time of the two versions of the algorithm for building the CoNL structure, and the possibility to adopt some different specifications for computing the structural parameters of the model. The comparison is based on a dataset of 195 trajectories on 145 different origin-destination (o-d) pairs, tracked with the aid of an Android application. The trajectories have been collected through the smartphones of travellers moving within the network of Regione Campania (Italy). The results show the superiority of the CoNL route choice model in reproducing observed route choices when compared with other commonly used route choice formulations.]]></description>
      <pubDate>Sat, 08 Aug 2020 16:01:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1709071</guid>
    </item>
    <item>
      <title>Conventional Optimization Technique Based Methodology to Estimate Gap Acceptance Functions</title>
      <link>https://trid.trb.org/View/1721013</link>
      <description><![CDATA[The definition of a critical gap in literature supports that it not only varies from driver to driver but also for (over) rejected gaps by a driver. In this light, multinomial probit (MNP) models were proposed in the past as gap acceptance functions. There are few methodologies available in the literature to estimate these functions. These methodologies are either computationally expensive or involve sophisticated mathematics. This is possibly one of the reasons due to which MNP models are not explored extensively as a tool to model critical gaps. Further, estimation of such gap acceptance functions always leads to maximization of likelihood functions. This paper proposes a methodology based on a conventional optimization technique for the first time to estimate the parameters of a class of gap acceptance functions. It is also shown that the estimation results match with those from another method, namely CHOMP. The proposed method turns out to be a simple yet effective one.]]></description>
      <pubDate>Wed, 29 Jul 2020 16:15:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1721013</guid>
    </item>
    <item>
      <title>Generalized multinomial probit Model: Accommodating constrained random parameters</title>
      <link>https://trid.trb.org/View/1654864</link>
      <description><![CDATA[Skewed and constrained distributions for model parameters are better suited to provide realistic taste sensitivity and Willingness-To-Pay (WTP) distributions in many empirical applications. In this context of random taste heterogeneity, the standard multinomial probit (MNP) has seen limited applicability largely owing to the normal distributional assumption of model parameters that will invariably result in (a) counter-intuitive taste sensitivities for a significant proportion of the population, and (b) WTP distributions without finite moments. In this paper, a Generalized MNP (GMNP) model that allows constrained random parameters with multivariate truncated normal distribution was developed. The ability of the maximum simulated likelihood inference method to retrieve the model parameters was demonstrated on synthetic data using the GHK simulator with quasi-Monte Carlo sequences. The bias in the parameters of the MNP model that ignores the constraints on model parameters was also demonstrated. Also, the proposed model was used to analyze car parking preferences in Jerusalem while ensuring (a) negative taste sensitivities for cost and time attributes, and (b) finite moments of the WTP measures associated with walk and search times.]]></description>
      <pubDate>Sat, 16 Nov 2019 17:06:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1654864</guid>
    </item>
    <item>
      <title>Impact of weather conditions on middle school students’ commute mode choices: Empirical findings from Beijing, China</title>
      <link>https://trid.trb.org/View/1594374</link>
      <description><![CDATA[Weather conditions have been recognized as important factors affecting school commute mode choices. This paper aims to explore the modal shift of middle school commutes with respect to the variation in weather-related variables, with empirical emphases on the situation in Beijing, China. Data from the latest Beijing School Commute Survey (2014–2015) were adopted, and multinomial probit (MNP) and multinomial logit (MNL) models were developed. The modeling results are in favor of the MNP model because it has better statistical performance. Weather-related variables, including sky condition, wind speed, highest temperature, humidity, air quality index (AQI), and some interaction terms, were found to have a significant impact on students' commute mode choices. Based on these models, an empirical sensitivity measure was defined as the expected percentage change in the probability of choosing each mode with respect to an order of magnitude change in the influential factors. Most of the results are in line with those of previous studies, and some unique results reflect features of Beijing. For example, on days with extremely poor air quality, students are more likely to turn to public transport rather than use a car from active transportation modes. This is probably due to the special urban traffic regulations that restrict household car ownership and car travel in Beijing. These findings could have implications for promoting active transportation for students and serve as references for policymakers and planners.]]></description>
      <pubDate>Fri, 29 Mar 2019 10:15:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1594374</guid>
    </item>
    <item>
      <title>Generalized Multinomial Probit Model: Accommodating Constrained Random Parameters</title>
      <link>https://trid.trb.org/View/1572921</link>
      <description><![CDATA[Skewed and constrained distributions for model parameters are better suited to provide realistic taste sensitivity and Willingness-To-Pay (WTP) distributions in many empirical applications. In this context of random taste heterogeneity, the standard multinomial probit (MNP) has seen limited applicability largely owing to the normal distributional assumption of model parameters that will invariably result in (a) counter-intuitive taste sensitivities for a significant proportion of the population, and (b) WTP distributions without finite moments. In this paper, a Generalized MNP (GMNP) model that allows constrained random parameters with multivariate truncated normal distribution was developed. The ability of the maximum simulated likelihood inference method to retrieve the model parameters was demonstrated on synthetic data using the GHK simulator with quasi-Monte Carlo sequences. The bias in the parameters of the MNP model that ignores the constraints on model parameters was also demonstrated. Also, the proposed model was used to analyze car parking preferences in Jerusalem while ensuring (a) negative tastes for cost and time attributes, and (b) finite moments of the WTP measures associated with walk and search times.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1572921</guid>
    </item>
    <item>
      <title>A Multivariate Model of Ride-Hailing Trip Characteristics in Dallas, TX</title>
      <link>https://trid.trb.org/View/1572552</link>
      <description><![CDATA[Even as ride-hailing becomes ubiquitous in most urban areas, its impacts on individual travel are still unclear. In particular, there is limited knowledge on the potential impacts on the use of other modes, peak-period travel, and new induced trips. The current study contributes to this knowledge gap by investigating ride-hailing trip characteristics through a multi-dimensional model estimated using data collected from the Dallas-Fort Worth area in the fall of 2017. Model results are used as initial evidence to the discussion about potential positive and negative externalities of ride-hailing adoption. The results suggest a need for policies that discourage the substitution of short-distance “walkable” trips by ride-hailing, and a need for low cost and well-integrated multi-modal systems to avoid substitution of transit trips by ride-hailing. More generally, the results in this study reveal that ride-hailing is fundamentally changing the spatial, temporal, and modal activity-travel landscape of individuals.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1572552</guid>
    </item>
    <item>
      <title>An Integrated Choice and Latent Variable Framework to Incorporate the Influence of Travel Time Variability on Truck Route Choice</title>
      <link>https://trid.trb.org/View/1572545</link>
      <description><![CDATA[This study proposes a multinomial probit (MNP)-based Integrated Choice and Latent Variable (ICLV) modelling framework that allows simultaneous estimation of route-level travel time variability and incorporation of the influence of such variability on route choice. The travel time on a route is considered as a latent variable and global positioning system (GPS) data measurements of route-level travel time are used to identify the parameters of its statistical distribution. Since such measurements are not always available for all routes, the latent variable component of the ICLV framework helps inform the travel time distribution for routes without travel time measurements. The proposed model is applied to an empirical data set on truck route choice using GPS data from Tampa, Florida. The empirical models account for the variability of travel time on a route as a function of the network structure along the route, such as the lengths of different roadway types. The route choice component of the proposed ICLV model demonstrates a superior statistical fit and better predictive ability than traditional choice models that do not consider the influence of travel time variability on route choice – both on the estimation dataset and a validation dataset. A reduced form mixed probit model was found to have a superior data fit than the route choice component of the ICLV model, albeit it fails to offer the insights that the proposed ICLV model offers on travel time variability its influence on route choice.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1572545</guid>
    </item>
    <item>
      <title>Trade-offs among planned versus performed activity patterns</title>
      <link>https://trid.trb.org/View/1509309</link>
      <description><![CDATA[This paper focuses on the development of a methodology to identify the latent factors leading to changes in the planned itineraries of travellers that result in their actual activity patterns. Specifically, the authors propose a way to utilise patterns of activities established by individuals across multiple days to generate possible alternative actions by these individuals when faced with conditions that produce a discrepancy between performed and planned patterns on a particular day. The choice alternatives, which are unobserved, are inferred by rules applied to comprehensive multiday data collected in Belgium, consisting of information regarding planned activity itineraries, performed activity/travel diaries, and demographics of travellers. These data are utilised to analyse and explore the underlying reasons preventing individuals from performing their planned activities on a given day, and to identify the influential parameters that lead individuals to trade their planned patterns with those actually performed. Using multiday data, the authors generate all possible combinations of categories of activities – mandatory, maintenance, discretionary, and pickup/drop off activities – that can form patterns for individuals. Under the assumption that the performed patterns have the closest utility to the planned patterns, the authors estimate the latent factors that influence travellers’ time use behaviour using a multinomial probit choice structure in which the covariance structure of the choice alternatives is specified in terms of the overlap in activities. The authors further identify the ‘costs’ associated with making changes in planned agenda (replacing, inserting, or deleting an activity). These penalty values are estimated using ‘Parallel Genetic Algorithm’, where the fitness function is the likelihood function estimated under the multinomial choice model structure. The results show that individuals’ mobility decisions related to mandatory activities are more robust than those associated with their non-mandatory counterparts.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509309</guid>
    </item>
    <item>
      <title>Accommodating a Flexible Response Heterogeneity Distribution in Choice Models of Human Behavior for Transportation Planning</title>
      <link>https://trid.trb.org/View/1505229</link>
      <description><![CDATA[In this report, the authors propose a general copula approach to accommodate non-normal continuous mixing distributions in multinomial probit (MNP) models. In particular, the authors specify a multivariate mixing distribution that allows different marginal continuous parametric distributions for different coefficients. A new hybrid estimation technique is proposed to estimate the model, which combines the advantageous features of each of the maximum simulated likelihood inference technique and Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach. The effectiveness of this formulation and inference approach is demonstrated through simulation exercises and an empirical application.]]></description>
      <pubDate>Tue, 24 Apr 2018 09:19:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1505229</guid>
    </item>
    <item>
      <title>New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function</title>
      <link>https://trid.trb.org/View/1502637</link>
      <description><![CDATA[In this paper, the authors develop a new matrix-based implementation of the Mendell and Elston (ME) analytic approximation to evaluate the multivariate normal cumulative distribution (MVNCD) function, using an LDLT decomposition method followed by a rank 1 update of the LDLT factorization. The authors' implementation is easy to code for individuals familiar with matrix-based coding. Further, the authors' new matrix-based implementation for the ME algorithm allows them to efficiently write the analytic matrix-based gradients of the approximated MVNCD function with respect to the abscissae and correlation parameters, an issue that is important in econometric model estimation. In addition, the authors propose four new analytic methods for approximating the MVNCD function. The paper then evaluates the ability of the multiple approximations for individual MVNCD evaluations as well as multinomial probit model estimation. As expected, in the authors' tests for evaluating individual MVNCD functions, the authors found that the traditional GHK approach degrades rapidly as the dimensionality of integration increases. Concomitant with this degradation in accuracy is a rapid increase in computational time. The analytic approximation methods are also much more stable across different numbers of dimensions of integration, and even the simplest of these methods is superior to the GHK-500 beyond seven dimensions of integration. Based on all the evaluation results in this paper, the authors recommend the new Two-Variate Bivariate Screening (TVBS) method proposed in this paper as the evaluation approach for MVNCD function evaluation.]]></description>
      <pubDate>Fri, 16 Mar 2018 09:52:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1502637</guid>
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