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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Idaho Truck Parking Research Project</title>
      <link>https://trid.trb.org/View/2693717</link>
      <description><![CDATA[Truck parking issues stem from the lack of available designated parking at convenient locations, and Federal Hours of Service (HOS) regulations of the Federal Motor Carrier Safety Administration (FMCSA) that require drivers to rest at specific intervals. Truck parking capacity has not kept up with the demand, leaving truck drivers with few options at the end of their shift or while waiting for pick-up and delivery windows. The Idaho Transportation Department (ITD) Truck Parking Research Project aims to inform the Statewide Freight Plan and provide solutions and recommendations to solve current and future truck parking demands to support freight movement and Idaho’s overall economy. This report studies the truck parking challenges and requirements and reviews similar truck studies in other states and presents findings from interviews with the Freight Advisory Committee, the Trucking Advisory Committee, and other trucking stakeholders on specific truck parking hot spots and general systemic challenges throughout the state. Public and private truck parking locations and capacity are analyzed with the development of a comprehensive Idaho truck parking database which is used to assesses truck parking demand using truck probe GPS data from the American Transportation Research Institute (ATRI). Finally, the needs for truck parking facilities based on current utilization, current and future unmet demand, and operational and policy needs are presented along with truck parking investment recommendations.]]></description>
      <pubDate>Fri, 17 Apr 2026 11:54:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693717</guid>
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    <item>
      <title>Pilot Duty Time Policy Measures: a Meta-analysis Approach</title>
      <link>https://trid.trb.org/View/2674214</link>
      <description><![CDATA[Flight safety is critical for commercial aviation operation. According to statistics, 80% of aircraft incidents or accidents are attributed to human factors. Within human factors, fatigue is one of the key aspects to investigate, using pilot duty time as a proxy variable. This study conducted a literature review and utilized data from studies to redefine variables and apply a meta-analysis approach. The results reveal that mental and physical health have a positive correlation with duty time, while short- to medium-haul flights negatively impact duty time. In summary, this study highlights the significant influence of pilot duty time on fatigue, emphasizing the need for better fatigue management and updated regulations, particularly for short- and medium-haul flights. Future research should expand its focus to underrepresented regions, such as Asia and Africa, and explore alternative analytical methods to provide a more comprehensive understanding of the issue.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674214</guid>
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    <item>
      <title>Assessing the Impact of Flexi-Time Schedules on Morning Peak-Hour Congestion: A Cox Proportional Hazard (CPH) Approach</title>
      <link>https://trid.trb.org/View/2659315</link>
      <description><![CDATA[Fixed-time work schedules have significantly increased peak-hour travel demand in the post-pandemic, highlighting the need for a cost-effective travel demand management (TDM) strategy. Therefore, this study examines the impact of time flexibility on travel demand by analysing departure time differences between fixed and flexi-time employees and identifying the key determinants influencing their departure choices. Using the Cox proportional hazard model, this study analyses data from Greater Kuala Lumpur, Malaysia, collected between June 11 and July 21, 2023. The findings indicate that fixed-time employees are major contributors to peak-hour congestion, with a 50% shift to flexi-time can reduce congestion by 7.78%. Departure choices of fixed-time employees are significantly influenced by more factors than flexi-time employees. Managerial, technical, clerical, and administrative occupations are more adaptable to flexi-timing, whereas healthcare and manufacturing roles are less suited due to operational constraints. These insights can assist transport policymakers in developing more effective TDM strategies.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659315</guid>
    </item>
    <item>
      <title>Qualitative assessment of traffic congestion issues in car-oriented cities: Insights from Malaysian commuters</title>
      <link>https://trid.trb.org/View/2655514</link>
      <description><![CDATA[Traffic congestion in car-oriented regions leads to longer travel times, increased emissions, and reduced commuter well-being. This study examines key factors contributing to private car dependency from the perspective of workers in Greater Kuala Lumpur, Malaysia, using data collected between June 10 and July 20, 2023. A combination of word cloud generation, thematic categorisation, sentiment analysis, and binary logistic regression was employed to analyse commuter responses. Despite around 70% of respondents living within 400 meters of public transport, more than 80% still rely on private vehicles. Barriers such as poor last-mile connectivity, delays, and inadequate infrastructure were frequently cited. Fixed-time workers experience heightened vulnerability due to rigid schedules and congestion-related delays, while flex-time workers report service inefficiencies during off-peak hours. The findings suggest that enhancing public transport reliability, infrastructure, and scheduling, along with supporting flexible work policies, can reduce car dependency and alleviate congestion in urban, car-dominated regions.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655514</guid>
    </item>
    <item>
      <title>Understanding employee and managerial acceptance of flexible working arrangements as a transport policy in the Philippines: A sectoral comparative analysis</title>
      <link>https://trid.trb.org/View/2635115</link>
      <description><![CDATA[Using behavioral theories, such as the modified unified theory of acceptance and use of technology (UTAUT), we extended the analysis of non-infrastructure travel demand management (TDM) strategies, such as flexible work arrangements (FWA), in managing the urban mobility of employees and managers beyond the constraints of classical transport policy evaluation. This study uses the classical UTAUT constructs – performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and personal attitude (PA) – and additional psychological constructs, subjective wellbeing (SWB), work-life balance (WLB), and employee satisfaction (ES), to capture the underlying nuances, motivations, and concerns regarding the behavioral intention (BI) of adopting FWA. We further conducted a multigroup analysis to investigate whether the acceptance behavior of key stakeholders in the government and private sectors differ significantly. The findings suggest that the significant influence and perceived benefits of PA, PE, and WLB among employees and managers reveal the potential impacts of FWA in decongesting metropolitan urban spaces, especially during peak hours, reinforcing the idea of employing non-infrastructure TDM policies to help alleviate the worsening traffic conditions in major thoroughfares of the Philippines, as stipulated in the National Transport Policy. Additionally, these results identify the key areas of concern for both employees and managers affecting FWA adoption, enabling policymakers and organizational leaders to formulate more equitable sector-specific policies and guidelines to help achieve and sustain the identified potential benefits of time and cost savings, improved work performance, better quality of life, and positive environmental outcomes.]]></description>
      <pubDate>Wed, 25 Feb 2026 17:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635115</guid>
    </item>
    <item>
      <title>Assessing the economic impacts of labour time in autonomous vehicles</title>
      <link>https://trid.trb.org/View/2618636</link>
      <description><![CDATA[Previous studies have analysed the impacts of the introduction of autonomous vehicles on transport networks and estimated the safety, congestion, freight, parking and vehicle ownership impacts to social welfare and the economy. However, there appears to be a gap in the literature on the economic impacts of individuals allocating travel time in an autonomous vehicle to labour activities. This additional labour time could then have resulting impacts to productivity and the broader economy. This paper addresses this gap through the development of a novel microeconomic model incorporating time use in autonomous vehicles. The model captures an individual’s consumption behaviour, demand for leisure and supply of labour while accounting for the allocation of travel time to labour and leisure. It is an extension of existing microeconomic models of time use for two features: (1) travel utility, and (2) labour while travelling. This model is then implemented in an integrated computable general equilibrium and transport model for Sydney, Australia, and is tested to understand the order of magnitude of impacts. From this model, the increase in autonomous vehicle penetration rate and the resultant increases to household budget from travelling wages will result in a total welfare increase but with a decreasing rate, which are mainly due to congestion effects. Interestingly, the congestion effects also result in the production and value of time first increase, followed by a decrease, which are counter intuitive.]]></description>
      <pubDate>Wed, 11 Feb 2026 09:17:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618636</guid>
    </item>
    <item>
      <title>Platform-induced time-space trade-offs in ride-hailing: Multi-homing as a response to operational constraints</title>
      <link>https://trid.trb.org/View/2643809</link>
      <description><![CDATA[This study examines how ride-hailing drivers adjust their time-use and spatial behavior under platform-induced constraints, with a focus on multi-homing—the practice of operating across multiple ride-hailing platforms. Drawing on a city-scale, driver-identified dataset from Suzhou, China, we propose a data-driven framework to identify multi-homing behavior and quantify its impacts using four operational metrics: working hours, travel distance, revenue, and order interval. A common assumption is that full-time multi-homing drivers earn more and work longer than single-platform drivers. However, our results show that this assumption does not hold in the Suzhou market. Instead, multi-homing appears to serve as a behavioral adaptation to regulatory and algorithmic restrictions—allowing drivers to bypass platform-imposed work-hour caps and optimize engagement with temporal demand fluctuations. Using clustering to separate full-time and part-time drivers, and applying Geographically Weighted Random Forest (GWRF) modeling, we further find that multi-platform activity is not spatially concentrated in low-demand or remote areas. These findings reveal that multi-homing is less about spatial expansion and more about temporal strategy and coping with institutional uncertainty. The study contributes to understanding time-space adaptation in digitally mediated mobility, especially amid evolving platform governance. It also underscores the need for time-use models and transport policy to account for the real-time flexibility and constraint navigation strategies employed by gig workers in fragmented digital environments.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:53:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643809</guid>
    </item>
    <item>
      <title>The periodic vehicle routing problem with multi-day trips</title>
      <link>https://trid.trb.org/View/2642063</link>
      <description><![CDATA[This work proposes the Periodic Vehicle Routing Problem with Multi-Day Trips, a new routing problem variant that is inspired by regional distribution operations in the industrial gases sector. In this problem, customers provide multiple visiting patterns, a.k.a. schedules, comprising specific days within the planning horizon when they expect to receive product. The goal is then to assign each customer to one valid schedule and design the corresponding routes for each day such that the distribution costs are minimized across the horizon. In our setting, we allow for long-haul routes that cannot be concluded within the course of one shift and the driver must have one or more layovers, in conformance to applicable hours-of-service regulations. To that end, we introduce a set partitioning model and propose a branch-price-and-cut algorithm, extending the classical periodic vehicle routing solution approach to accommodate the multi-day trips. Our approach is able to properly capture the connections between the time periods such that, along with the normal daily routes, the pricing subproblems and their dynamic programming solver also generate routes that extend across multiple days and are compliant with the regulations, all the while ensuring that customers are visited in accordance to their offered schedules. Instances with schedule-dependent demands, a.k.a. service choice, are also supported. We extend literature benchmark instances as well as use an industrial case study to assess the performance of our approach, which we show can routinely solve to guaranteed optimality instances with 20 customers along a 6-day planning horizon.]]></description>
      <pubDate>Tue, 30 Dec 2025 09:46:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642063</guid>
    </item>
    <item>
      <title>Overworked and understaffed: Prioritising fatigue risk factors based on seafarers’ perspectives</title>
      <link>https://trid.trb.org/View/2633818</link>
      <description><![CDATA[Fatigue is considered a major safety concern in the shipping sector. It is imperative to understand its underlying determinants, considering that it is the most identifiable and preventable cause of accidents. Although studies have investigated a diverse range of risk factors, there is limited research on those that should be prioritized. To address this, the study employed an online survey in which seafarers were prompted to rank eight predetermined fatigue risk factors based on their perceived severity. An open-ended question allowed the respondents to identify additional factors. A total of 4,974 valid ranking responses were analyzed, and content analysis was used for 1096 qualitative comments. Most respondents were male (94.8 %); the average age was 38.0 years, with a mean of 14.2 years of seafaring experience. The respondents spanned 106 nationalities, 111 flag States, 26 ship ranks, and 23 ship types. Workload was ranked as the primary risk factor, followed by long working hours. This result was consistent with the 1,096 qualitative comments, which identified operational factors (i.e. work demands and work hours) as the most prominent theme. Crewing levels emerged as the most frequently occurring code in the open-ended responses, underlining its fundamental role in mitigating seafarers’ fatigue. The study recommends prioritizing two critical factors: workload and work hours. Focusing on these specific factors by reinforcing crewing and work/rest hours regulations can significantly help reduce seafarers’ fatigue. This strategic approach is essential for strengthening the foundational layer of the fatigue regulatory framework and maintaining an equitable playing field in the complex shipping industry.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633818</guid>
    </item>
    <item>
      <title>Unlocking the potential of cooperative staggered shifts in urban networks</title>
      <link>https://trid.trb.org/View/2604660</link>
      <description><![CDATA[Staggered shifts strategies effectively alleviate traffic pressure and promote the rational allocation of traffic resources by dispersing peak-hour traffic demands. The development of advanced traveler information systems (ATIS) platforms has facilitated the rapid transmission and precise delivery of traffic information. Current studies have combined ATIS platforms with staggered shifts strategies to propose cooperative staggered shifts (CSS) strategies, which can enhance the sophistication of staggered shifts strategies and, consequently, improve their effectiveness. However, current studies on CSS inadequately consider the heterogeneity in the willingness of travelers with different travel behaviors to adjust their departure times. Additionally, existing studies have used traffic state optimization as the sole objective function, without considering system costs. To fill this gap, this study integrates multi-source spatiotemporal big data and survey data to analyze the willingness of travelers with different travel behaviors to adjust their departure times. Based on this analysis, a modeling framework for CSS that considers system costs is constructed. The framework is designed with the dual objectives of optimizing traffic conditions and minimizing system costs. Using the fast-solving algorithm proposed in this study for large-scale scenarios, the Pareto front of the CSS framework is analyzed. Taking Hangzhou city, China as an example, the results indicates that an 11.1% optimization effect on the traffic state can be achieved with only 2.4% of the maximum system cost; As the system cost increases, the marginal benefits of CSS diminish. The research findings can provide effective support for the modeling and policy formulation of CSS strategies.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604660</guid>
    </item>
    <item>
      <title>Vehicle routing and scheduling under hours of service regulations: A review</title>
      <link>https://trid.trb.org/View/2603790</link>
      <description><![CDATA[Hours of Service (HOS) regulations establish maximum limits on truck drivers’ working and driving hours, and demand compulsory break and rest periods. Many countries adopt such regulations as a means to enhance road safety and improve working conditions. In this work, we present a systematic literature review of vehicle routing and scheduling problems that consider HOS regulatory constraints in their models and algorithms. We examine how these driver restrictions are addressed and solved in the transportation science literature by analyzing and discussing 41 papers published between 2000 and 2024. In our review, we identified five different regulations from distinct countries that are addressed in the literature. The papers were analyzed and discussed from multiple perspectives, including modeling, solution methods, and problems variants, as well as specific aspects of the regulations considered in each work, such as the different sets of legal rules considered. Finally, we identify opportunities for further research concerning HOS regulations in routing and scheduling problems.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603790</guid>
    </item>
    <item>
      <title>Critical commuters and staggered working hours strategies: A two-stage framework integrating machine learning and social equity</title>
      <link>https://trid.trb.org/View/2614594</link>
      <description><![CDATA[Congestion remains one of the most prevalent transport problems in major cities, with social equity being a pivotal aspect in shaping solutions. This study presents a two-stage framework that combines machine learning with social justice principles to identify critical commuters who can adjust their workplace arrival times. This framework addresses both traffic management and social equity concerns, considering synchronisation needs at the employer, household, and individual levels. Our proposed framework can identify critical commuters based on their basic information collected by the employer. We benchmark multiple machine learning approaches to model and predict an individual’s ability to shift their workplace arrival times. Ultimately, we frame the problem as a classification task and select gradient boosting due to its superior performance. Using employee survey data from Rennes Metropole in France, we identify the key factors that influence individual’s flexibility in their arrival times. Regular school drop-offs are the most significant factor, followed by theoretical arrival time contracts with employers and, to a lesser extent, age and income. Building on these findings, we apply the Rawlsian “Min–Max” fairness principle rooted in social science to refine the subpopulation of commuters with theoretical shift abilities and assess their practical likelihood of shifting within a socially equitable framework.By integrating machine learning insights with social equity considerations, this framework offers an interdisciplinary approach to potentially mitigate congestion, ensuring that policies not only address traffic demand management but also promote fairness and inclusion, supporting their long-term effectiveness.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:53:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614594</guid>
    </item>
    <item>
      <title>Night Versus Day Work—Balancing Safety, Operations, and Constructability for Short-Term Operations on Two-Lane Roads</title>
      <link>https://trid.trb.org/View/2600988</link>
      <description><![CDATA[Allowable work hours (AWH), also known as Allowable Closure Hours or Limitations on Operations, for a contract dictate which hours of the day construction and maintenance activities may occur and are known to affect operations, constructability, and safety. Individual districts set AWH for activities—including short-term maintenance, paving projects, construction contracts, and Land Use Permit work by utilities and other permittees—on two-lane roads in Virginia. AWH decisions balance numerous factors, including queue length, delay, crash risks, noise, nighttime constructability, worker safety, and local ordinances, among others. Because AWH are set locally, the process for setting AWH and the resulting AWH for similar roads vary within and between districts. This project assessed the Virginia Department of Transportation’s (VDOT's) current AWH practices in Virginia on two-lane roads for short-term projects, the level of variability among practices, and opportunities to improve statewide consistency. Researchers developed and distributed a statewide survey on AWH, conducted interviews with representatives from other state departments of transportation, and analyzed data from Virginia’s pavement management scheduling system. The results identified benefits to setting AWH at the district level rather than implementing statewide AWH. The work also identified specific areas that may benefit from statewide consistency without overly affecting local flexibility on decision making. The report recommends developing a framework for decision making and conducting outreach in regard to the existing decision-making tools.]]></description>
      <pubDate>Mon, 22 Sep 2025 11:58:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2600988</guid>
    </item>
    <item>
      <title>Algorithms for pickup and delivery problems with hours of service constraints</title>
      <link>https://trid.trb.org/View/2564234</link>
      <description><![CDATA[The authors propose new exact and heuristic algorithms for solving an extension of the Pickup and Delivery problem with Time Windows that considers numerous constraints encountered in the real world. The problem involves optimally routing a fleet of identical vehicles to service a set of pickup and delivery pairs subject to capacity, time window, pairing, precedence, and last-in-first-out loading constraints as well as complex driver rules. The authors consider a set partitioning model based on routes, and also introduce a formulation based on fragments which are segments of routes with a particular structure. Computational results on randomly generated instances are used to compare the scalability of the two formulations with respect to the number of requests. A technique for reducing the number of routes or fragments is proposed which relies on a machine learning model to determine those that are likely to be in the optimal solution. When the number of routes or fragments is reduced using the machine learning model, high quality solutions are obtained on the instances solvable by the exact method. Solutions can also be obtained for instances where route or fragment generation is intractable.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:54:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564234</guid>
    </item>
    <item>
      <title>Routing Short-Haul Trucks Under the Uncertainties of Travel Time and Service Time [supporting dataset]</title>
      <link>https://trid.trb.org/View/2592194</link>
      <description><![CDATA[This dataset supports research on optimizing battery electric vehicle (BEV) fleet dispatching in last-mile freight logistics under uncertainty. It accompanies the study on the Electric Vehicle Routing Problem with Backhauls and Time Windows under Travel Time and Service Time Uncertainty (EVRPBTW-USUT), which extends previous research by incorporating a backhauling strategy and modeling uncertainty in travel and customer service times. The dataset consists of 60 benchmark instances, derived from the well-known EVRPTW dataset, with varying customer sizes and backhaul proportions, enabling robust evaluations of routing strategies. Additionally, a real-world dispatching dataset from a full-service supply chain company in San Bernardino County, California, is included to validate the approach in practical applications. Each instance is provided in CSV format, with detailed solutions recorded in Excel files. These datasets support the development and benchmarking of optimization algorithms, particularly for robust vehicle routing and sustainable urban freight logistics.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:53:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592194</guid>
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