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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>
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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>E-Commerce Middle-Mile Network Design with Delivery Speed Choices and Service Level Constraints</title>
      <link>https://trid.trb.org/View/2709159</link>
      <description><![CDATA[The increasing demand for expedited e-commerce deliveries, with delivery times of one to three days, highlights the importance of optimizing the middle-mile network. Most retailers store a considerable portion of their inventory at the regional distribution centers (RDCs) outside urban areas, from where it is moved to the customer zones equipped with last-mile distribution facilities as required. Thus, RDC locations become critical in middle-mile operations, directly impacting the transit times to customer zones and, ultimately, the delivery times in the last mile. This paper presents a middle-mile network design problem arising in the context of e-commerce companies in the presence of customers with different delivery time preferences. Specifically, it allows RDCs to satisfy demands from customer zones using delivery times longer than requested, albeit with penalties, if that helps reduce cost without violating the service level requirements of fulfilling at least a given threshold of the demands within the requested delivery times. The problem is formulated as a mixed-integer linear program, for which an exact Lagrangian relaxation-based branch-and-bound algorithm is proposed. Several enhancements to the algorithm are provided, including an efficient Lagrangian heuristic for the primal-bound, a Benders decomposition framework to solve one of the Lagrangian subproblems efficiently, an analytical approach for obtaining Benders optimality cuts, and a partial analytical characterization of Pareto-optimal Benders cuts. With these enhancements, our final algorithm substantially outperforms the state-of-the-art commercial solver, as highlighted by our computational experiments on an extensive set of 220 instances with up to 80 potential RDC locations and 1,000 customer zones. Our best algorithm solves 204 of the 220 instances to 0.50% duality gap compared with only 108 that CPLEX could solve to the same gap within an allowed 10-hour CPU time limit. Furthermore, it achieves an average time savings of 63.24% compared with CPLEX across all the instances.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709159</guid>
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    <item>
      <title>Relaxing or imposing constraints? Exploring the associations between digitalization and space-time flexibilities in Qinghe, Beijing</title>
      <link>https://trid.trb.org/View/2714872</link>
      <description><![CDATA[While information and communications technology (ICT)-based multitasking has become a routine feature of daily activity organization, its implications for space-time constraints remain insufficiently understood. This study develops ICT-based multitasking by distinguishing foreground and background ICT use and examines how different forms of ICT use are associated with perceived temporal and spatial flexibility at the activity and daily levels. The analysis draws on a 2-day combined daily activity diary and internet activity diary survey conducted in 2021 in Qinghe, Beijing. In particular, records from both diaries are integrated to distinguish background and foreground ICT use. The associations of foreground and background ICT use with space-time flexibility are estimated by multilevel ordered logistic regression at the activity level and ordinary least squares regression with standard errors clustered at the daily level. Analyses are conducted for light and heavy ICT users separately because they are considered to have different ICT use patterns. Results demonstrate that foreground and background ICT use are associated with perceived flexibility in contrasting ways across analytical scales and user groups. At the activity level, both forms of ICT use are associated with higher perceived temporal and spatial flexibility, particularly among light ICT users. At the daily level, more frequent foreground ICT use is associated with higher average flexibility, whereas frequent background ICT use among heavy ICT users is associated with lower perceived temporal flexibility. These findings highlight the importance of distinguishing foreground and background ICT use when assessing how digitalization relates to activity-travel behavior in the mobile ICT era.]]></description>
      <pubDate>Thu, 16 Jul 2026 16:38:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714872</guid>
    </item>
    <item>
      <title>Time-use behaviour in the United Kingdom: a comparative analysis of pre-COVID19 and during COVID19</title>
      <link>https://trid.trb.org/View/2662685</link>
      <description><![CDATA[The COVID-19 pandemic triggered profound shifts in daily activity patterns and time allocation, providing a unique opportunity to study behavioural adaptations during unprecedented disruptions. This paper examines changes in time-use behaviour in the United Kingdom by comparing pre-pandemic and pandemic periods using data from the UK Time Use Survey (UKTUS) for 2014–2015 and 2020–2021. A Multiple Discrete-Continuous Extreme Value (MDCEV) model is employed to analyse how individuals allocate time across different activities and locations. The findings highlight significant increases in participation and duration of in-home activities, particularly work, shopping, and leisure, while out-of-home activities, such as work, study, and travel, experienced notable declines. The marginal utility analysis reveals that in-home work and shopping surpassed their out-of-home counterparts, reflecting adaptations to pandemic restrictions. Moreover, generational differences in time-use patterns diminished, indicating more uniform behavioural adjustments across age groups. The study identifies challenges faced by larger households in accommodating remote work and study, exacerbated by space constraints and competing demands. Persistent gender disparities are also observed, with women disproportionately engaged in home care and personal care activities, constraining their participation in remote work. On average, women spent 66–71 more minutes per day on home care and 7–13 more minutes per day on personal care than men across both periods.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662685</guid>
    </item>
    <item>
      <title>Dynamic Arrival Prioritization With Target Time Management and Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2658908</link>
      <description><![CDATA[The European Air Traffic Management system is among the most complex systems in the world. Due to the dense nature of the European network, consequences of disruptions are often catastrophic. In particular, disruptions altering the expected flying time tend to pose great challenges to the arrival management of busy hubs. In response, EUROCONTROL released the Target Time Management (TTM) system, allowing airlines to issue Target Times of Arrival (TTA) even before depart. The TTM system helps hubs airports coordinate arrivals and departures. From the point of view of airlines, the advantage resides in being able to prioritize early arrivals of critical flights. Nevertheless, real-time prioritization is not trivial. Many studies have focused on this problem but with results limited to slot swapping in a tactical context. This is less effective compared to airlines having the ability to select a new slot at the pre-tactical level. This work covers this gap, allowing airlines to select the desired TTA even before departure. We use Deep Reinforcement Learning to create a dynamic arrival allocation model capable of prioritizing flights in terms of passenger connecting time, curfew performance, rotation delay, and fairness to other airlines. Additionally, the model is capable of adapting and react to the uncertainty in responses from the TTM. In the real-world, large anticipations in TTAs are often rejected. The model is tested with real data from SWISS International Airline. Results show an improvement of 5.9 minutes for critical passenger connection and 4.8 minutes for rotation delay versus a deterministic approach.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658908</guid>
    </item>
    <item>
      <title>Exploring the relationship between e-shopping and goods delivery via transport superapps and daily time use: insights from Indonesian cities</title>
      <link>https://trid.trb.org/View/2669580</link>
      <description><![CDATA[Various information and communication technology (ICT) services, such as e-shopping and goods delivery, have transformed individuals’ daily activities by reshaping how people allocate their time. Within transport super-apps (TSAs), e-shopping and goods delivery are typically offered together but represent distinct services, with e-shopping involving online purchasing and goods delivery referring to the transport of items independent of a retail transaction. While most studies focus on how e-shopping and goods delivery impact physical activities like in-store shopping, this study explores how these services associated with broader individual time allocation across various activities and locations. This research uses TSAs which are multifunctional, in Indonesia as a case study and includes non-TSA users as a reference group. The study employs a one-week time-use and app-use diary from TSA users and non-users across four Indonesian cities, analysed with a multiple discrete–continuous extreme value model with inverse probability weights. The analysis suggests that shop and delivery services are associated with rebound effects, which influence how time is allocated across various activities and locations, and vary according to socio-demographic and residential characteristics. Increased use is associated with more time spent on at-home mandatory activities among workers and greater leisure participation among men and individuals of working age. In larger cities such as Jakarta, more frequent use of these services is associated with longer durations of out-of-home mandatory activities. Further, the results indicate that out-of-home TSA usage is associated with reduced time spent on in-home leisure and increased engagement in out-of-home leisure activities, suggesting that individuals who are already active outside the home may integrate digital services into their existing activity patterns. In contrast, in-home TSA usage is associated with lower participation in out-of-home activities, indicating that individuals who rely on in-home services may be less inclined to engage in activities outside the home. The relationships between service usage and time allocation appear to vary depending on whether the services are used at home or outside, highlighting the importance of locational context. The findings suggest that integrated mixed-use developments and the provision of local leisure spaces can better align daily activity patterns with the use of TSA services, reflecting how digital platforms reshape the organisation of activities and associated travel demand. At the same time, location-specific strategies, such as promoting targeted e-shopping adoption in smaller cities, expanding accessible in-store options in larger cities, and prioritising sustainable delivery technologies in megacities, are essential to manage rebound effects and decarbonise urban travel and logistics systems.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669580</guid>
    </item>
    <item>
      <title>Learning to retrieve containers: A scale-diverse deep reinforcement learning approach for the container retrieval problem</title>
      <link>https://trid.trb.org/View/2643810</link>
      <description><![CDATA[This study addresses the container retrieval problem (CRP), a key challenge in the storage yards of automated container terminals where operational efficiency directly affects vessel turnaround time and yard congestion. In storage yards, containers are stacked vertically to maximize space utilization; however, accessing one located below others requires relocating the blocking containers, leading to additional crane movements and delays. The CRP involves retrieving containers from multiple bays in a specified order while minimizing the total working time of the yard crane, with relocation position decisions being critical. The CRP poses several practical challenges: despite being 𝒩𝒫-hard, real-world instances often involve hundreds of containers, requiring high-quality solutions in real time; yard configurations also vary widely and change frequently, demanding methods that adapt effectively to arbitrary layouts. We propose a novel deep reinforcement learning approach incorporating (1) a size-agnostic network architecture, enabling a single trained network to handle diverse yard configurations, and (2) a scale-diverse learning framework, which trains on a various yard scales using a normalized loss to improve generalization and scalability. Experiments on well-known benchmarks with several hundred containers show that the proposed method substantially outperforms existing baselines across a wide range of yard sizes. It also scales to instances with thousands of containers and maintains strong performance in dynamic settings where retrieval orders are revealed online. Solutions are produced within a second for realistic instances, confirming its effectiveness and practical applicability in real-world automated container terminals. The implementation and datasets used in this study are publicly available in the GitHub repository: https://github.com/operagang/CRP_RL.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643810</guid>
    </item>
    <item>
      <title>How does home-based teleworking reshape commuting and activity participation? An empirical study in Shanghai using machine learning</title>
      <link>https://trid.trb.org/View/2654574</link>
      <description><![CDATA[Despite the growing adoption of teleworking, empirical studies remain limited in developing nations, including China. However, the rapid rise of teleworking, accelerated by the COVID-19 pandemic, highlights the need to understand its impacts. This study examines how teleworking influences commuting behavior and time reallocation, identifying key factors and their effects. Using survey data from Shanghai, we first analyze commuting differences across teleworking patterns, revealing significant effects on travel modes, departure times, weekly commuting days, and trip-chaining complexity. We then develop XGBoost models to investigate how individuals reallocate saved commuting time across four activity types: in-home, out-of-home mandatory, leisure, and maintenance activities. SHAP and Partial Dependence Plot analyses identify four core factors—age, daily Internet use, commercial housing density, and company density—as critical determinants of activity choices. The findings suggest that while teleworking may reduce commuting trips, it primarily restructures travel patterns rather than simply decreasing trip frequency. These findings will help transport planner develop adaptive measures to accommodate evolving mobility behaviors in an increasingly telework-oriented society.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2654574</guid>
    </item>
    <item>
      <title>Time Budget for Merchant Ship Control Takeover - Preliminary Results</title>
      <link>https://trid.trb.org/View/2624136</link>
      <description><![CDATA[The increasing automation of shipping requires a proper understanding of the behavior of those in command of ships to be able to correctly mimic and interpret their actions. This will be especially important during the widespread introduction of autonomous merchant vessels, whose decision-making algorithms will need to be correctly prepared to assess situational awareness and will allow for timely control takeover in a variety of circumstances, including mixed navigation conditions. Therefore, this research aims to investigate the safety-critical situations when the assistance of a captain is required by the watch officer. There can be various situations when such a person is called and asked to proceed to Bridge and exercise his/her experience to help the younger and less skilled colleague in a potentially dangerous situation. In the study, we asked experienced Masters Mariners about their perception of such a situation. We investigated whether it depends on their sea-time experience and other factors (e.g. traffic density, weather conditions, fatigue) in a particular situation on board the vessel. The results of this study may prove valuable in determining the time required for obtaining a situation awareness during control takeover in different situations. The collected results may also prove useful in designing and developing navigation simulator exercise scenarios in the context of assessing situational awareness or providing control-taking guidelines for Maritime Autonomous Surface Ships (MASS).]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624136</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>Locomotive Team Productivity as a Criterion for Optimal Locomotive Fleet Management</title>
      <link>https://trid.trb.org/View/2407936</link>
      <description><![CDATA[The problem of the transportation process technological regulation within the framework of extraterritorial models for managing locomotive fleets can include solving the complex problem of finding the optimal parameters for controlling the work of locomotive crews serving the train operation of freight traffic within the specific territory boundaries. The purpose of this study was to substantiate the feasibility of including such basic criteria for solving this problem as the average daily productivity of the locomotive fleet, labor productivity indicators of locomotive crews, as well as to develop an approach to modeling the crews’ working time. It is proposed to consider the maximum labor productivity of the locomotive crews’ workers contingent at the specific territory in freight and passenger traffic and the value of their hourly output as one of the criteria for the train operation management quality. It is possible to control the characteristics of the locomotive crews’ labor productivity through the cycle time of their work. It is shown that the choice of a rational cycle time allows the alignment and synchronization of the production operations’ duration for performing gross ton-kilometer work. In this regard, the opportunities for modeling the value of the locomotive crews’ operating time on the basis of the study of factors that have both a random and constant influence on the takt time appear.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407936</guid>
    </item>
    <item>
      <title>Toward mainstreaming care activities in transportation: a time use and mobility segmentation approach</title>
      <link>https://trid.trb.org/View/2561692</link>
      <description><![CDATA[This paper assesses the importance of incorporating care dimensions into activity-travel segmentation to understand daily life mobility strategies. The data came from six neighbourhoods in Concepción, Chile, and included detailed information on activity-travel time use and interaction and classification schemes to identify care purposes. The study uses self-organizing maps to build incremental behavioural segments from weekly mobility and time use variables, adding care activities to assess their role in this segmentation. The results identify groups with a higher burden on care than others, emphasizing the role of transport mode and time use patterns. The result remarks caregiving activities hidden within other categories, identifying groups of caregivers, including domestic workers and women who work and have intense accompanying activities with children. The results highlight the differences between mobility patterns between different segments to make more invisible care and other related activities disproportionately performed by groups of women.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561692</guid>
    </item>
    <item>
      <title>Parametric design of time-sensitive routing with recipient-dependent contributions</title>
      <link>https://trid.trb.org/View/2564184</link>
      <description><![CDATA[Last-mile delivery complexities intensify for perishable goods, which must maintain quality, mainly when transported on non-refrigerated vehicles. If recipients are unavailable, delivery failure may prolong the delivery of perishable goods, thus jeopardizing their integrity. This study proposes recipient-dependent last-mile delivery solutions for perishable goods with time-sensitive delivery routes where the recipients contribute to the process. The authors explore applications of Autonomous Vehicles (AVs) in recipient-dependent deliveries of perishable goods and compare traditional truck delivery with a proposed AV pickup policy and other multi-echelon routing policies. The authors propose a parametric design of the policies, characterizing each policy by a set of variables inspired by the network design literature. In this study, routes are regarded as length-constrained, which is essential for the time-sensitive delivery of perishable goods. The authors compare the optimal cost of policies in length-bounding (time-sensitive) with capacity-bounding routes. A detailed dominance space analysis highlights the optimal policy under various cost structures and shows that the status quo for truck delivery is dominated as the number of deliveries increases. Increasing hand-off costs also lead to the dominance of AV and hybrid policies over traditional truck delivery. The authors validate the proposed managerial insights through a case study of a Walmart location delivery service in Toronto, proving the applicability of the models. This research contributes to the strategic integration of AVs in last-mile delivery of perishable goods.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564184</guid>
    </item>
    <item>
      <title>An Activity-Journey-Network Approach for Modelling Travel Behaviour of Multiple User Classes Under Time Constraints</title>
      <link>https://trid.trb.org/View/2577219</link>
      <description><![CDATA[The flow pattern on a given transportation network at a given moment results from many users’ travel decisions which are made for some purposes, for example, participating in necessary activities such as work, eating and shopping. Consequently, the explicit modelling of the interaction between users’ activity and travel choice behaviour serves as a basic building block for long-term transportation planning and management. In this paper, an activity-based network user equilibrium model is proposed to study the dynamic activity-travel scheduling problem with multiple classes of users under time constraints. A simple supernetwork representation approach is introduced to generate the activity-journey-network (AJN) which expands the basic transportation network in both time and space dimensions. With the supernetwork representation, the dynamic activity-travel scheduling problem is transformed into a static network flow assignment problem. A heuristic algorithm is developed to find the path with the maximum utility in the AJN from the start node to the end node for each user. A numerical study is conducted to illustrate the application of the proposed model and solution algorithm for several transportation networks including large-scale real-world networks. It is shown that both the individual’s travel choice and group travel behaviour in a transportation network can be well studied by the proposed model.]]></description>
      <pubDate>Fri, 25 Jul 2025 11:32:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577219</guid>
    </item>
    <item>
      <title>Pricing and Demand Management for Integrated Same-Day and Next-Day Delivery Systems</title>
      <link>https://trid.trb.org/View/2552285</link>
      <description><![CDATA[The authors study a system in which a common delivery fleet provides service to both same-day delivery (SDD) and next-day delivery (NDD) orders placed by e-retail customers who are sensitive to delivery prices. The authors develop a model of the system and optimize with respect to two separate objectives. First, empirical research suggests that fulfilling e-retail orders ahead of promised delivery days increases a firm’s long-run market share. Motivated by this phenomenon, the authors optimize for customer satisfaction by maximizing the quantity of NDD orders fulfilled one day early given fixed prices. Next, the authors optimize for total profit; optimize for a single SDD price, and then set SDD prices in a two-level scheme with discounts for early-ordering customers. The authors' analysis relies on continuous approximation techniques to capture the interplay between NDD and SDD orders and particularly the effect one day’s operations have on the next, a novel modeling component not present in SDD-only models; a key technical result is establishing the model’s convergence to a steady state using dynamical systems theory. The authors derive structural insights and efficient algorithms for both objectives. In particular, the authors show that, under certain conditions, the total profit is a piecewise-convex function with polynomially many breakpoints that can be efficiently enumerated. In a case study set in metropolitan Denver, Colorado, approximately 10% of NDD orders can be fulfilled one day early at optimality, and profit is increased by 1% to 3% in a two-level pricing scheme versus a one-level scheme. The authors conduct operational simulations for validation of solutions and analysis of initial conditions.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2552285</guid>
    </item>
    <item>
      <title>The ridesharing routing problem with flexible pickup and drop-off points</title>
      <link>https://trid.trb.org/View/2557159</link>
      <description><![CDATA[In major metropolitan areas, ride-sharing systems can help reduce traffic congestion and increase the transportation system’s efficiency. In this paper, the authors propose a Branch-and-Price based approach for solving the ride-share routing problem with flexible pickup and drop-off points. The authors assume a ride-sharing system where drivers have their own origins and destinations, where all the drivers’ and passengers’ information is known beforehand, and all the problem data information is static and deterministic. The authors assume that drivers can pick up or drop off passengers from or to flexible meeting points that are within a passenger’s walking time limit from their origin or destination and are determined on a continuous plane. The authors formulate a mixed integer nonlinear model for routing and selecting pickup and drop-off points. The authors' solution approach decomposes this problem in two: selecting pickup and drop-off points and a rideshare routing problem. The authors develop an efficient algorithm to select the best pickup and drop-off points and show computationally that it is more efficient at finding pickup and drop-off points than considering a fixed set of discrete meeting points. To evaluate the performance of their approach, the authors perform numerical experiments on a San Francisco Taxicab dataset. Results show that the authors' approach is efficient, solving instances with up to 600 points within 31 CPU minutes. For these datasets, incorporating flexible pickup and drop-off points can reduce the total vehicle travel time of the rideshare system by 4% on average.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2557159</guid>
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