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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Learning nonlinearity and measuring uncertainty--a multi-task neural network and additive gaussian process based travel choice model</title>
      <link>https://trid.trb.org/View/2659403</link>
      <description><![CDATA[Discrete choice model (DCM) is a classical framework for modelling an individual’s travel choice. However, its oversimplified architecture of utility function may limit its performance when faced with a complex decision process. In this paper, we develop a new framework called multi-task neural network and additive gaussian process based discrete choice model (MNNAGP-DCM). Specially, the multi-task neural network (MNN) is used to learn the representation of individual characteristics, while the additive Gaussian process regression (AGP) process is utilized to enhance flexibility of utility function. In multi-task neural network, the sub-learners learn the taste parameters between individuals’ characteristic in each alternative, while the global bias term is used to learn the cross effect between alternatives. The additive GPR framework is employed to substitute the linear term in utility function with a nonparametric probability framework. Additive GPR enables the modelling of nonlinearity, threshold effects and uncertainty, thereby providing a more comprehensive perspective on the decision-making process. Moreover, when combined with DCM, the GPRs become intractable. To address this, we employ variational inference to construct a tractable lower bound, thereby transforming the original model into a tractable one. Then MNNAGP-DCM can be optimized by gradient based algorithms such as Adam. The proposed model is tested on the open-source dataset and benchmarked with standard MNL, Mix-logit, XGBoost, TasteNet-MNL, MNN-DCM and MNNSGP-DCM. Results show that MNNAGP-DCM can not only capture individuals’ heterogeneity but also can learn the nonlinearity in utility function, showing great superiority in terms of predictability. Our model can also provide interpretable result with taste parameters and the fitted GPR models, while quantifying uncertainty through GPR’s probability framework.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659403</guid>
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    <item>
      <title>Mode choice for leisure travel in Europe: Simulating future transport policies</title>
      <link>https://trid.trb.org/View/2686389</link>
      <description><![CDATA[The European travel sector is experiencing a transformation driven by increased climate awareness and policy measures aimed at reducing external costs such as emissions. This study examines how Swiss travelers respond to these developments, using a stated preference experiment including the modes of train, night train, car, and airplane. Employing nested logit models, we find a significant willingness-to-pay for sustainable aviation fuel (SAF) of CHF 94 per ton of CO₂e. Based on the estimated coefficients, we evaluate the impacts of four policy scenarios: an aviation tax (CHF 30), a night train subsidy (CHF 20), a SAF blending quota, and a market outlook for 2030. These scenarios are benchmarked against the first-best Pigovian tax on transport externalities. Assessing demand shifts, consumer surplus, and external costs, we find that subsidizing night train prices, the aviation tax, and the 2030 scenario increase welfare, whereas a 6% SAF mandate reduces it.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686389</guid>
    </item>
    <item>
      <title>Beyond Box-Cox: A diffusion-inspired functional framework for nonlinear demand and discrete choice modeling</title>
      <link>https://trid.trb.org/View/2643825</link>
      <description><![CDATA[This paper presents a new diffusion-inspired functional framework for modeling nonlinear demand and discrete choice, with applications in transportation and related fields. The framework goes beyond traditional transformations by combining a diffusion-like core function with upper and lower bound functions, linked through a transfer function. Key properties such as monotonicity and concavity or convexity are guaranteed by simple, verifiable conditions on the component functions. A key advantage of this approach is that it breaks the nonlinear structure into separate parts, making it easier to integrate socio-economic variables. This enables the model to capture non-linear shifts, damping, and scaling effects across different socio-economic groups. The framework is low-parametric, bounded, continuous, and modular, which makes it easy to estimate using standard software. Its flexibility and strengths are illustrated in two case studies: (i) a discrete choice model of transport mode selection, and (ii) a nonlinear logistic regression model of vehicle ownership. In both cases, the framework enhances model fit, facilitates better control over tail behavior, demonstrates how heterogeneity can be effectively integrated, and yields more precise and behaviorally plausible elasticity estimates. Controlled simulations further demonstrate the framework’s robustness across a broad range of nonlinear processes. Adjusting the individual sub-components leads to distinct functional behaviors, preventing convergence toward a single common shape. This diversity indicates that the framework avoids ”copy-cat” behavior or functional collapse. As a result, its flexible, bounded structure can be tailored or relaxed depending on the application, offering virtually limitless possibilities for adapting functions to address different problems.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643825</guid>
    </item>
    <item>
      <title>Willingness to pay and trading behaviour of mobility credits</title>
      <link>https://trid.trb.org/View/2668532</link>
      <description><![CDATA[Tradeable Mobility Credits (TMC) are a novel demand management policy. Travel can be priced based on externalities and travellers are allocated TMC, which are consumed when travelling, with the price depending on trip characteristics. Travellers can buy/sell TMC in exchange for money. In this study, we analyse (1) how travel behaviour would be affected by a TMC-scheme, (2) TMC trading behaviour and (3) their interaction. We carry out an online stated preference survey, and apply a latent class choice model (LCCM) to analyse travel behaviour, whereas credit trading is analysed by means of a multiple linear regression. A key finding throughout the research is that TMC tend to be perceived non-linearly, with a logarithmic transformation often outperforming linear specifications. This means each additional credit carries less value. The LCCM reveals three out of four groups (88 % of respondents) consider their current balance when making travel choices. Two groups (∼50 %) are predominantly unimodal, travelling almost exclusively by bicycle or public transport. Others base their decision primarily on travel time and cost. In trading, the exchange rate and balance have a substantial influence, offering evidence for loss aversion. The number of travel instances remaining, and the experience of having performed a trade in the past also affect trading behaviour, whereas socio-demographic characteristics are found to have a limited impact. Our result show a TMC policy can achieve substantial behavioural adaptations, reaching the desired outcomes. The limited awareness of such policies, concerns about equitable TMC allocation and additional hassle associated with trading remain challenges to be addressed.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668532</guid>
    </item>
    <item>
      <title>Exploring Cycling Behavior Shifts among Young Adults through a Longitudinal Cohort Survey</title>
      <link>https://trid.trb.org/View/2683116</link>
      <description><![CDATA[Young adults often use sustainable transportation options, such as public transportation, cycling, and walking for daily transportation. However, evidence on the retention of sustainable travel behaviors is unclear, and longitudinal analysis of travel behavior changes among young adults is scarce. The disruption in travel behavior caused by the COVID-19 pandemic, and significant attention toward the promotion of active transportation during and after the pandemic, offered an opportunity to explore this topic in a quasi-experimental setting. We utilized survey data from two waves (baseline and follow-up) of a longitudinal online cohort of 552 respondents in the Greater Toronto and Hamilton Area, Canada, who were post-secondary students in 2019. Using the data, we explored the association between changes in commute-related cycling frequency (unchanged, started cycling, and stopped cycling) and respondents’ socio-demographic characteristics, pre-pandemic travel satisfaction, and life events during the pandemic years. About 8% of respondents self-reported that they started cycling for commuting after the pandemic, and another 6% reported that they stopped cycling. Results from a discrete choice multinomial logit model indicate that younger age and pre-pandemic travel satisfaction with active transportation modes were associated with higher odds of starting to cycle after the pandemic. Furthermore, starting full-time work was associated with higher odds of stopping cycling for commuting purposes. Moving residence to more urban locations was associated with higher odds of starting to cycle, but this association was not statistically significant when other factors were taken into account.]]></description>
      <pubDate>Sun, 22 Mar 2026 17:18:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683116</guid>
    </item>
    <item>
      <title>Modeling the Non-Identical Perception Variance in Day-to-Day Dynamics via Weibit-Based Network Loading Function</title>
      <link>https://trid.trb.org/View/2591225</link>
      <description><![CDATA[The Weibit choice model has gained increasing attention in transportation studies. Compared with the commonly used Logit model, the Weibit model inherently captures the heterogeneous travel perceptions by allowing non-identical variances for different alternatives. Nevertheless, how travelers’ heterogeneous perception errors may influence day-to-day (DTD) network dynamics, in which route choice decisions are made on each day, remains underexplored. In this study, we present several deterministic discrete DTD dynamic traffic models with Weibit-based network loading function, termed Weibit-based DTD dynamic models. We provide the asymptotic stability conditions of the Weibit stochastic equilibrium states based on the Jacobian matrices of the dynamical systems. We demonstrate how the features of non-identical perception variances and asymmetric response curve of the Weibit model can influence the evolution of network states and eventually the equilibrium points of the dynamical systems compared to the Logit case. Under fair comparison, the equilibrium states of Weibit DTD models are shown to have larger stable regions of adjustment rates than those of Logit DTD models. This research contributes to understanding the significance of considering travelers’ non-identical perception errors in DTD dynamics.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591225</guid>
    </item>
    <item>
      <title>Charging mode choice of electric micromobility users under uncertainty</title>
      <link>https://trid.trb.org/View/2642446</link>
      <description><![CDATA[The need to develop new charging practices for electric micromobility vehicles (EMVs) is inevitable due to the challenges associated with charging and managing their batteries. Intelligent battery swapping services for EMVs have expanded from being business-to-business to business-to-customer. This battery swapping model offers the advantages of convenience and battery controllability, effectively addressing the current charging issues faced by EMVs. To assess the feasibility and potential success of promoting intelligent battery swapping services, this study conducts a stated choice experiment to investigate the charging behavior of EMV users across different modes, such as self-operated charging, charging at charging stations, and intelligent battery swapping. We propose a charging and swapping choice modeling approach that combines cumulative prospect theory and multi-attribute decision making methods to model charging decisions under uncertainty. The results demonstrate that our proposed method surpasses conventional models in terms of model goodness-of-fit and behavioral interpretation. Furthermore, heterogeneity was observed among EMV users in their charging and swapping behaviors. Based on these findings, we discuss policy implications for creating sustainable cities, with a particular focus on establishing intelligent battery swapping facilities.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642446</guid>
    </item>
    <item>
      <title>Bridges matter: Modeling joint bridge and route choice equilibrium with bridge-centric choice set generation</title>
      <link>https://trid.trb.org/View/2632509</link>
      <description><![CDATA[As a critical infrastructure in the road network divided by rivers, bridges are an inevitable consideration in traveling between disjointed network sections. Despite the increasingly recognized importance of bridges in routing, the perception and choice of bridges have not received enough attention in travel demand modeling, such as the widely used stochastic user equilibrium (SUE) traffic assignment (TA) model. This study develops a joint bridge and route choice equilibrium model to account for various behavioral issues stemming from the bridge choice dimension. The perceived availability of bridges owing to their spatial relationships with origins and destinations is specifically considered in both bridge set formation and bridge choice behavior from the perspective of travelers. The interaction between bridge and route choice dimensions is modeled via a hierarchical choice structure. The network equilibrium is modeled consistently with joint bridge and route choice behavior and formulated as an equivalent mathematical programming problem. To facilitate the application of the proposed equilibrium model in bridge networks, we develop a bridge-centric choice set generation strategy with a multi-stage column generation algorithm that can effectively employ properties of bridges and routes. Numerical experiments are conducted to demonstrate the applicability of the proposed model and choice set generation strategy in a real-world setting. The model’s tested performance underscores its potential as a policy instrument for informed decision-making in the assessment and management of bridge networks.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632509</guid>
    </item>
    <item>
      <title>The behavioral dimension of transport decarbonization</title>
      <link>https://trid.trb.org/View/2657928</link>
      <description><![CDATA[Achieving effective decarbonization requires technological innovation and understanding of behavior. Drawing on an interdisciplinary workshop, this paper emphasizes integrating behavioral insights into climate policy design to ensure technical effectiveness, social acceptability, and equity. We propose a framework combining behavioral data, choice modeling, agent-based simulation, and optimization to assess policy impacts under deep uncertainty. Although focused on transport, the approach generalizes across sectors.]]></description>
      <pubDate>Thu, 26 Feb 2026 11:55:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657928</guid>
    </item>
    <item>
      <title>Modeling EV charging behavior with hybrid choice models</title>
      <link>https://trid.trb.org/View/2624236</link>
      <description><![CDATA[Smart charging programs adjust the time of day of electric vehicle charging to reduce congestion in the grid, reduce the cost of electricity, and maximize renewable energy use. As General Motors partners with electric utilities to design and implement these programs, it is important to understand customers’ preferences and motivations for enrollment. A discrete choice experiment was conducted to quantify how customers trade off among program characteristics and incentives when deciding whether to enroll in demand response, managed charging, and fixed schedule programs. The data were used to estimate two hybrid choice models, which outperform a benchmark conditional logit model and systematically account for unobserved preference heterogeneity using discrete–continuous mixtures. The results indicate that monetary incentives and environmental benefits increase the likelihood of choosing a smart charging program. Meanwhile, there is notable preference heterogeneity regarding non-monetary program options and enrollment perks. Latent environmental concern was constructed as a useful dimension for differentiating customers: those with higher environmental concern tend to exhibit higher valuations of reducing emissions and maximizing renewable energy use. We further linked preferences to five personas based on sociodemographic clustering. The findings enable targeted marketing efforts that highlight the environmental benefits of smart charging to the customer groups most likely to be environmentally concerned.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:23:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624236</guid>
    </item>
    <item>
      <title>Transferring Household Evacuation Choice Behavioral Models to Create a Digital Twin for Future Storm Responses: Opportunities and Challenges</title>
      <link>https://trid.trb.org/View/2672024</link>
      <description><![CDATA[This research studied whether and how hurricane evacuation behavioral models, estimated with survey data collected from previous storms, could be used in predicting evacuation patterns in a new storm setting (with an anticipation of being able to conduct real-time simulation in the future). With publicly available data, this study first created synthetic populations for the study region (i.e., New Orleans, Louisiana) by year. Findings from this process show that: 1) simulating evacuation behavior can only be done for storms that have occurred between 2013 and two years back from the current year; and 2) it might not be appropriate to use population data of a different year to simulate evacuation behavior in a current storm year owing to population migration. With synthetic populations created for 2021, this study simulated household hurricane evacuation-related choices in Hurricane Ida with behavioral models estimated before, which facilitates discussions about model transferability. It was found that lognormal distance function parameters in the evacuate/stay and departure timing joint choice model, and destination risk perception values in the destination choice model are the two most critical factors that need to be updated. Both factor updates are related to storm characteristics and can be completed with live storm feeds, which indicates real-time data input is indispensable in improving prediction accuracy. This study highlights data challenges for real-time simulation, helps improve the usefulness of estimated statistical models in practical applications, and emphasizes the importance of considering human components (including demographic profiles and choice behavior) in creating digital twins.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672024</guid>
    </item>
    <item>
      <title>Parking Demand Assignment Model: A Dual Utility-Based Approach</title>
      <link>https://trid.trb.org/View/2647801</link>
      <description><![CDATA[Balancing parking supply and demand is a fundamental goal of effective parking management, which depends first and foremost on accurately estimating parking demand before allocating it to available supply. This study addresses the demand-supply equilibrium from a micro-level perspective, focusing on the process by which individual parking spaces are selected and utilized. To model this, a dual utility-based framework is proposed that simulates the assignment of parking demand to specific spaces. The approach begins by defining a utility function to quantify the attractiveness of parking spaces in relation to different land uses. This is followed by the application of a logit model to estimate the probability of each parking space being chosen, thereby enabling the assignment of demand based on these probabilities. While traditional studies often focus on macro-level comparisons between zonal supply and demand, such approaches may overlook detailed behavioral patterns that influence how parking is actually used. By shifting the focus to the micro level, this study captures spatial and behavioral nuances that affect the real-world allocation of parking demand. A hypothetical case study is used to demonstrate the applicability and validity of the proposed model, showing how it can reveal unmet demand, utilization patterns, and inefficiencies in existing supply. These insights are critical for supporting data-driven parking policies, including supply expansion, demand management strategies such as pricing, or a combination of both. The structure of the paper includes an introduction to the problem, a review of relevant literature, a formal problem definition, and the presentation of the proposed methodology. This is followed by the case study and a discussion of the results, leading to conclusions that highlight the model’s value for improving parking planning and policy at a granular level.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647801</guid>
    </item>
    <item>
      <title>Characterization of Activity-Travel Time Allocation for Urban Transportation Disadvantaged Groups: Taking Chongqing Citizen Groups as an Example</title>
      <link>https://trid.trb.org/View/2613300</link>
      <description><![CDATA[This paper explores the activity-time travel characteristics of urban disadvantaged groups that consider personal, family, and activity transportation attributes in influencing individual activity choices. This study uses detailed individual travel activity information from the 2018 Chongqing Municipal Resident Travel Survey. In order to precisely circumscribe the study scope, we first use second-order clustering to fuse individual transportation travel characteristics to obtain transportation disadvantaged groups. Then, we use the multiple discrete-continuous extreme value (MDCEV) modeling framework to model the travel activity characteristics of disadvantaged and advantaged groups and explore the role of the influence of different types of variables. It is found that (1) there is a significant difference in travel activity behavior between the groups, regardless of preference or time-saturation effect; and (2) the attributes of active transportation (travel distance and commuting time) are found to have the most significant influence role in the model of Chongqing city.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613300</guid>
    </item>
    <item>
      <title>The emergence of rideshare buddy-pooling behavior: Dynamic user choices and mobility platform operation decisions</title>
      <link>https://trid.trb.org/View/2622397</link>
      <description><![CDATA[Ride-pooling services have become globally popular due to their cost-effectiveness, offering discounted fares for passengers with similar origins and destinations. However, these fares are typically higher than if the cost of a single ride was evenly split among passengers. As a result, many travelers are now utilizing social media or group chats to find appropriate rideshare partners themselves, and then request a single ride as a group. This practice, henceforth referred to as ‘buddy-pooling behavior’, is particularly popular among certain communities such as residents and college students, who seek suitable partners within their community for rideshare to city centers, airports, and train stations. This paper presents a comprehensive doubly dynamical framework (considering within-day and day-to-day dynamics) to model, optimize, and evaluate the decision-making processes of travelers, drivers and ride-sourcing platforms with the buddy-pooling behavior. The proposed model considers several typical travel options, including ride-pooling (facilitated by the ride-sourcing platform), buddy-pooling (organized by customers themselves through social media or third-party matching service), multihoming for both ride-pooling and buddy-pooling, non-pooling services, and public transit. Our study begins by examining the time-dependent decision-making of travelers and drivers within a day, subsequently characterizing the evolution of the system on a day-to-day basis. Furthermore, we introduce a bi-level framework to optimize the pricing strategies of ride-sourcing platforms with the aim of enhancing system efficiency and platform profitability. Our results demonstrate that the emerging buddy-pooling behavior will benefit the drivers, travelers and the overall system. Moreover, the results suggest that multihoming behaviors combined with buddy-pooling behavior have the potential to generate more profit for the ride-sourcing platform. The proposed doubly dynamical model offers a framework to model and simulate the operational strategies of ride-sourcing platforms across different time periods, effectively capturing the complex choice behaviors of travelers.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622397</guid>
    </item>
    <item>
      <title>Mixed logit applications in travel behaviour research</title>
      <link>https://trid.trb.org/View/2619212</link>
      <description><![CDATA[In the past 20 years, mixed logit models have become mainstream for analysing choice behaviour. This paper analyses common approaches to capture travel behaviour using mixed logit models. Based on a sample of mixed logit applications from 2004 to 2024, the paper investigates to what extent the applications address seven critical issues in mixed logit modelling. The results show a high variation in how researchers address these issues in applied work. The paper also analyses whether there are time trends in the published literature. One trend is that more applications have used mixed logit models in recent years, but these applications address the seven issues less frequently than previously. However, the downward trend disappears after 2012, where, on average, papers from 2012 to 2015, 2016 to 2017, 2022, and 2024 address the issues to a similar extent. Based on this analysis, the paper suggests five modelling aspects relevant to mixed logit applications. In conclusion, the paper highlights the necessity for transport research applying mixed logit models to consider why it applies a specific mixed logit approach, what kind of distributions it applies, how it addresses systematic heterogeneity, how robust the analysis is, and finally, how to validate the models.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:51:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2619212</guid>
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