<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Exploring Latent and Manifest Effects on Micromobility Mode Choice</title>
      <link>https://trid.trb.org/View/2772601</link>
      <description><![CDATA[Shared modes of micromobility play an increasingly important role in urban transportation. Although mode choices on bike-sharing systems (BSS), shared electric scooters (ES) and short-distance public transit (PT) have been examined in recent years, there is a lack of joint analyses, especially considering latent choice influences such as environmental consciousness. To address this gap in knowledge, we estimate a hybrid choice model (HCM) with a nested mixed multinomial logit choice component for the mode choice between BSS, ES, and PT that fits the data with an adjusted rho-squared value of 0.45 and results in plausible parameter estimates. As a result of our joint consideration of BSS, ES, and PT, we find that respondents with a higher environmental consciousness are more likely to use BSS and PT than ES, even though all three modes are often considered as being used by travelers with a high environmental awareness. Additionally, we find that mode choice is nested in sheltered and unsheltered modes with PT in the sheltered nest and BSS and ES in the unsheltered nest. Furthermore, we find that smartphone or bag holders may not increase the utility for BSS and ES and that the remaining ES battery capacity does not appear to have a large effect on ES choice.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772601</guid>
    </item>
    <item>
      <title>End consumers’ delivery choice in an omnichannel e-retail context: a multinomial logit model</title>
      <link>https://trid.trb.org/View/2731060</link>
      <description><![CDATA[E-commerce is rapidly changing the behaviour of end consumers for purchasing and receiving e-parcels, leading to a significant increase in home deliveries. This phenomenon results in new challenges for cities such as more frequent, smaller, and failed deliveries and geographical sprawl, increasing traffic impacts, and reduced liveability and sustainability. In the new omnichannel e-retail context, end consumers can choose between several delivery channels, including crowdshipping, delivery to parked cars (into trunk), autonomous delivery robots, autonomous parcel lockers, and staffed pickup points. Each alternative has different implications for end consumers and cities. Although some studies have pointed out how end consumers make choices when faced with new alternatives, several aspects remained unexplored. This paper contributes by investigating end consumers’ preferences for multiple delivery channels through a stated preference survey. Several attributes are considered including destination location, delivery time and price, time windows, data protection, tracking and tracing service, potential damage, payment timeframe, flexibility, and level of sustainability. The findings highlight that end consumers have limited knowledge of new delivery channels and limited willingness to receive e-purchases away from home.]]></description>
      <pubDate>Fri, 28 Aug 2026 14:41:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731060</guid>
    </item>
    <item>
      <title>A stated preference survey for designing the physical elements of communities</title>
      <link>https://trid.trb.org/View/2713837</link>
      <description><![CDATA[Developing designs for the physical elements of communities involves both complexity and trade-offs. The human computer interface and generative design (GD) potentially address this challenge and present an opportunity for improved efficiency as compared to designing based solely on a human planner. The purpose of this paper is to design and test a repeated Stated Preference (SP) survey, as part of a broader GD decision-support process, that obtains feedback on land use distribution and road network design from stakeholders. The results from the SP survey are quantified into binary logit model parameter estimates that are used as inputs for subsequent rounds of the GD process. This iterative process allows for the improvement of community designs with each round of consultation until a desired design outcome is reached. This use of SP survey methodology within a GD process to produce community designs that reflect the goals of the stakeholders, is novel. This paper provides a demonstration and preliminary application of the method.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:25:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713837</guid>
    </item>
    <item>
      <title>The Meteorological Modulators: Quantifying Weather’s Influence on Activity-Travel Behavior</title>
      <link>https://trid.trb.org/View/2761976</link>
      <description><![CDATA[This study analyzes the effects of weather conditions on travel demand by developing an integrated database that combines travel data with monthly, daily, and hourly weather information. The travel data were extracted from the 2018–19 CMAP Household Travel Survey, while weather information was obtained from the US Local Climatological Data provided by the National Centers for Environmental Information of the National Oceanic and Atmospheric Administration. The Chicago O’Hare International Airport weather station was used as the sole reference station, reflecting the study’s focus on temporal rather than spatial variability. This study examines the effects of a comprehensive set of weather metrics on activity-travel patterns, focusing on activity participation and mode choice behavior. Descriptive analyses of daily and hourly weather conditions and travel demand reveal how activity-travel behavior changes under varying weather conditions. The empirical analysis applies a mixed logit modeling framework to exclusively capture the impacts of hourly weather conditions and their interactions with demographic and built environment characteristics. An elasticity analysis is employed to assess the magnitude of weather-related effects. The results show that temperature, relative humidity, windspeed, and visibility strongly influence activity-travel behavior, particularly discretionary activity participation and active mode choices. In contrast, mandatory activity participation and single-occupancy vehicle choices are found to be less sensitive to weather variations. Overall, the findings highlight weather as a critical determinant of travel behavior, underscoring the need for dynamic travel demand models using high-resolution weather data and targeted transportation policies and infrastructure that support resilient transportation systems.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:25:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761976</guid>
    </item>
    <item>
      <title>Discrete Choice Model with Generalized Additive Utility Network for Interpretable Policy Evaluation</title>
      <link>https://trid.trb.org/View/2736657</link>
      <description><![CDATA[Discrete choice models (DCMs) are widely used for prediction and policy analysis, but standard multinomial logit (MNL) models with linear utility may fail to capture nonlinear response patterns in travel behavior. Neural extensions, such as deep neural network with alternative-specific utility functions (ASU-DNN), improve predictive flexibility, but their learned utility representations can be difficult to interpret and may be less stable under counterfactual shifts. This study proposes the generalized additive utility network (GAUNet) and its interaction variant, generalized additive and interactive utility network (GAIUNet), which represent systematic utility as a low-dimensional additive decomposition of learned neural shape functions. The proposed models are evaluated using synthetic transport mode choice data and real-world probe-person data collected in Tokyo, Japan, from 2018 to 2021. In synthetic experiments, GAUNet achieves predictive performance comparable to ASU-DNN and better than linear MNL, while yielding more stable policy evaluation under counterfactual changes in travel cost and access time. In real-world data, GAUNet and GAIUNet outperform linear MNL and remain competitive with ASU-DNN, while producing utility shapes that are easier to inspect at the attribute level. The results suggest that the proposed framework can recover interpretable nonlinear utility patterns without requiring cutoff locations to be specified in advance, thereby providing a transparent and flexible alternative to more entangled neural DCMs.]]></description>
      <pubDate>Thu, 30 Jul 2026 09:59:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736657</guid>
    </item>
    <item>
      <title>No remote, hybrid remote, and full remote: Additional insights from telework intensity</title>
      <link>https://trid.trb.org/View/2732584</link>
      <description><![CDATA[This study investigates the factors of telework intensity, no telework, hybrid telework, and full telework, using a generalized ordered logit model. The analysis integrates commuting characteristics, employer-based travel demand management strategies, and industry/occupation factors. Results indicate that telework intensity is primarily associated with occupational characteristics, workplace policies, and commuting conditions. Knowledge-intensive industries exhibit substantially higher levels of telework participation, whereas socio-demographic and built-environment factors contribute limited explanatory power. Elasticity analysis shows that automobile dependence significantly decreases the probability of teleworking, while longer in-vehicle travel time is negatively associated with higher telework intensity. Among employer-based strategies, flextime significantly increases telework participation, whereas compressed workweeks function as a substitute. These findings highlight the dominant role of job-related constraints and workplace flexibility in shaping telework intensity and illuminate the importance of differentiating telework participation by intensity level.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732584</guid>
    </item>
    <item>
      <title>Inferring trip purposes and demand dynamics in on-demand transit: A heuristic and probabilistic framework</title>
      <link>https://trid.trb.org/View/2670019</link>
      <description><![CDATA[On-demand transit (ODT) offers a flexible public transportation option that ensures mobility in underserved areas. However, the service is prone to failure due to inadequate planning of demand dynamics and market research. This study examines the influence of trip and neighborhood characteristics on ODT demand across various trip purposes, using data from a service provider in Downtown Memphis, Tennessee, USA. A novel heuristic and probabilistic trip inference method classifies ODT trips by purpose, incorporating variable walking radii to identify destination points of interest for trips with walking as the first and last mile. A two-stage nested logit model analyzes home-based (e.g., work, education, shopping) and non-home-based trips, revealing that wait times, trip distance, passenger numbers, and temporal factors like peak hours vary systematically across trip purposes. Neighborhoods with diverse racial groups (other than Black or White) show high ODT adoption, but areas with a high proportion of residents below the poverty line exhibit low usage, raising equity concerns. This research provides valuable insight for transportation planners and policymakers to make informed decisions in designing sustainable and inclusive ODT services.]]></description>
      <pubDate>Thu, 23 Jul 2026 17:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670019</guid>
    </item>
    <item>
      <title>Analyzing users’ preferences between personal and pooled rideshare services using a mixed logit modeling approach</title>
      <link>https://trid.trb.org/View/2658006</link>
      <description><![CDATA[Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in the transportation system in comparison to personal rideshare. Despite the growth in trip volume, ridesharing usage is still low compared to other popular transportation methods in the U.S., particularly traveling in one’s own personal vehicle. Furthermore, pooled rideshare usage is lower than personal rideshare. To understand riders’ preferences, a national survey (N = 2884) was conducted in the U.S. to investigate users’ choice behaviors in rideshare services examining personal versus pooled rideshare. Each survey respondent completed 20 stated-preference scenarios where participants choose between a personal or pooled rideshare option. Based on the responses, a mixed logit model was developed to capture the choice behavior preferences of the participants. The model unveiled the impact of demographic and trip attribute variables on users’ rideshare preferences. The discussion encompassed insights into demographic backgrounds and trip attributes, accompanied by a set of policy recommendations aimed at enhancing future pooled rideshare utilization.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658006</guid>
    </item>
    <item>
      <title>Customized bus routing problem considering co-opetition with taxis at transport hubs</title>
      <link>https://trid.trb.org/View/2623427</link>
      <description><![CDATA[At transport hubs, long taxi queues greatly degrade the passenger transfer experience, particularly at night when conventional buses and subways stop running. To address the difficulties in hailing taxis at transport hubs and improve hub transfer efficiency, we propose a combined mode referred to as the Customized Bus Plus Taxi (CBPT) service. In this mode, the customized bus (CB) transports passengers from the hub to bus stops closer to their destinations, where hailing a taxi is easier, which reduces both passenger travel time and costs. A logit model is constructed to capture passenger choices between CBPT services and taxi-alone services at hubs. A Mixed-integer Non-linear Programming (MINLP) model is formulated to solve the joint optimization problem for CB route and frequency design, considering time-varying demand. A customized gradient algorithm and a rolling horizon scheme are developed to solve the MINLP model. The application to Shanghai Hongqiao Hub demonstrates that the algorithm can solve a real-world problem in reasonable time. The results indicate that the CBPT service mode can reduce total costs by 43 % and waiting time costs by 84 % compared to using taxi services alone. The average taxi queuing time at the hub decreases from 50 mins to 8 mins. The CBPT service mode can greatly improve passenger transfer efficiency at the hub.]]></description>
      <pubDate>Thu, 18 Jun 2026 09:09:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623427</guid>
    </item>
    <item>
      <title>Modeling Travel Mode Choice Behavior on University Campus Using Nested Logit Analysis</title>
      <link>https://trid.trb.org/View/2676055</link>
      <description><![CDATA[The determinants of travel mode choice among university students and staff were examined to address a gap in campus mobility research, particularly within tropical environments. Data were obtained from 923 respondents at Mahidol University, Thailand, and analyzed through the application of the Nested Logit Model (NLM), which accounts for hierarchical decision structures across six travel modes: trams, bicycles, motorcycle taxis, private motorcycles, private cars, and walking. Exploratory factor analysis was employed to identify latent constructs influencing satisfaction, including comfort, built environment, and flexibility. The analysis indicated that active and shared modes, particularly trams and walking, were generally preferred. Travel time, cost, and scheduling flexibility were found to be key determinants of mode selection, with flexibility exerting a positive influence and travel time and cost acting as constraints. Weather-related factors were not statistically significant, suggesting that infrastructural conditions may mitigate climatic impacts on active travel. Elasticity analysis further demonstrated that changes in service attributes can prompt modal shifts between motorized and active travel. It is concluded that integrating attitudinal and contextual variables into discrete choice modeling offers a deeper understanding of mode choice behavior in campus environments. Policy implications include the enhancement of shaded pathways, the improvement of service reliability, and the adoption of flexible scheduling strategies to promote sustainable and health-supportive mobility. These findings provide a framework for the development of targeted campus transport policies in climate-sensitive settings.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676055</guid>
    </item>
    <item>
      <title>User-centric joint modeling of EV charging location preferences and charging needs</title>
      <link>https://trid.trb.org/View/2706386</link>
      <description><![CDATA[Electric vehicles (EVs) are widely regarded as having the potential to reduce emissions relative to internal combustion engine vehicles (ICEVs); however, charging dissatisfaction may discourage adoption and continued use. Strategically locating charging stations at users’ preferred sites is therefore crucial. This study jointly analyzes EV users’ preferred locations for high-quality chargers and their charging behaviors by estimating exploded logit-ordered logit models using nationwide survey data from Canada. Results show that individuals under 34 require high-quality chargers near residences, while older users prefer highway charging. Full-time employees, high-income households, larger families, and those with budget constraints demand improved workplace charging. Households with both ICEVs and EVs, as well as households with garages, rely more on out-of-home charging, while smart-charging subscription holders express dissatisfaction with home charging. Respondents from highly urbanized and less EV-mature provinces seek improved facilities near their residences. These findings can guide policymakers in strategically expanding EV charging networks.]]></description>
      <pubDate>Fri, 12 Jun 2026 09:19:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706386</guid>
    </item>
    <item>
      <title>Carbon credit incentives for heterogeneous green travel behavior: A Nanjing case study</title>
      <link>https://trid.trb.org/View/2704092</link>
      <description><![CDATA[Carbon credit strategies show promise for promoting sustainable travel, but uniform policies often neglect differential incentive effects stemming from traveler heterogeneity. To address this gap, our study integrates a Latent Class Logit model (LCL) with evolutionary game theory to propose a quantitatively differentiated carbon credit incentive strategy, using Nanjing as a case study. A Latent Class Logit model is employed to analyze the heterogeneous travel choice behavior mechanism and identify the targeted groups for incentives across various scenarios. An evolutionary game model framework is then developed to derive optimal credit values for all the heterogeneous groups. Results confirm carbon credits effectively encourage green modes and off-peak travel, particularly among time- and cost-sensitive groups (p < 0.01). Crucially, the evolutionary equilibrium reveals that setting incentives at 1–2 times baseline values optimally balances behavioral change and policy sustainability, providing empirically-grounded guidance for targeted intervention design.]]></description>
      <pubDate>Tue, 26 May 2026 13:19:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704092</guid>
    </item>
    <item>
      <title>Integrating network analysis and multinomial logistic regression for human error characteristics and influencing factors among train dispatchers</title>
      <link>https://trid.trb.org/View/2672594</link>
      <description><![CDATA[With increasing automation and operational complexity in recent railway systems, understanding dispatcher human errors has become essential for ensuring traffic safety. This study examined dispatcher human error characteristics and contributing factors in high-speed rail (HSR) and conventional rail (CR) dispatchers using 17,036 proactive safety inspection records. Human error co-occurrence networks for HSR and CR dispatchers were constructed via network analysis within the Human Factor Analysis and Classification System (HFACS) framework. Central error nodes and structural differences were identified through bridge analysis and network comparison test (NCT). The multinomial logit (MNL) model was employed to assess how selected factors associated with the likelihood of different high-frequency error types. Skill-based errors emerged as the most strongly connected nodes within and across HFACS levels in both networks. The NCT results showed significant differences between the HSR and CR networks in global structure, node centrality, and edge weights. In the CR network, inadequate supervision exhibited stronger co-occurrence with skill-based errors, decision errors, and crew resource management. The MNL results indicated that workload (jurisdictional station counts), task type, season, and shift were significantly associated with the likelihood of different error types (χ² = 6769.01, p < 0.001, Nagelkerke R² = 0.688). These findings may help inform dispatcher risk identification, task-specific interventions, and systemic safety management.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672594</guid>
    </item>
    <item>
      <title>Transit users’ preferences for street infrastructure during daytime and night-time walking</title>
      <link>https://trid.trb.org/View/2698581</link>
      <description><![CDATA[Understanding how street infrastructure influences route choices during access and egress walking trips is essential to promoting pedestrian-oriented environments around urban rail transit (URT) stations and extending their catchment areas. This study employed a visual route-choice experiment with 651 transit users in Guiyang, China, using a mixed logit model (MXL) incorporating interactions between individual characteristics and route attributes to evaluate preferences for street infrastructure during daytime and night-time walking. Results indicate that pedestrians favor routes to metro stations with narrower driveways, wider pavements, and tree- or hedge-lined walkways in the daytime. Adequate lighting and improved natural and mechanical surveillance effectively mitigate night-time security concerns. Preference heterogeneity was further observed across gender, age, and primary travel mode. The visualized experiment provides empirical evidence for designing pedestrian-friendly streets that enhance walking comfort and safety, thereby supporting sustainable transit use.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698581</guid>
    </item>
    <item>
      <title>Enhancing logit stochastic user equilibrium convergence in large‐scale networks via Barzilai–Borwein step size optimization</title>
      <link>https://trid.trb.org/View/2646703</link>
      <description><![CDATA[Traffic assignment serves as an important component in modeling flow distribution across infrastructure networks and supporting intelligent traffic management and urban planning. Fast algorithms for solving the stochastic user equilibrium (SUE) model are essential for enhancing computational performance and scalability of traffic assignment models applied to complex infrastructure networks. We augment the gradient projection (GP) algorithm for the SUE models through Barzilai–Borwein (BB) step size adaptation. For further optimizing computational performance, we explore iteration strategies within the GP algorithm: Jacobi parallelization, Gauss–Seidel sequential updating, and successive over-relaxation (SOR) with dynamic relaxation. Global convergence of these iterative methods in solving the SUE problems is theoretically established, with convergence conditions for the SOR derived. The results demonstrate the BB step size's superior performance, compared to alternative step size methods, across all network scales, and it achieves the best stability when demand factors and the dispersion parameter increase.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646703</guid>
    </item>
  </channel>
</rss>