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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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    <item>
      <title>DT-CTFP: 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction</title>
      <link>https://trid.trb.org/View/2617922</link>
      <description><![CDATA[In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617922</guid>
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    <item>
      <title>Extreme response scenarios for long-span bridges: Probabilistic generation and interpretable clustering under free-flow and congested traffic</title>
      <link>https://trid.trb.org/View/2672341</link>
      <description><![CDATA[Due to extended influence lines and diverse vehicle compositions, the loading mechanisms of long-span bridges differ fundamentally from those of short- and medium-span bridges. Prior studies often ascribe extreme effects to congested traffic, assuming that vehicle density and interactions control peak responses. Recent observations, however, indicate that certain free-flow states, such as nighttime periods with higher truck percentages or clusters of heavy trucks, can also trigger comparable extreme responses. These findings challenge the notion of universal congestion dominance and motivate a reassessment of how both traffic states contribute to extremes. Moreover, many traffic studies rely on full-duration simulations, implicitly treating all time segments as equally contributory. In reality, only a small subset of extreme-response scenarios (block maxima) governs structural extremes, yet such scenarios are rarely isolated and compared across traffic states. Therefore, as a novelty, this study introduces a probabilistic approach that simulates only the governing extreme scenarios to rapidly produce a large number of relevant samples of maximum traffic effects. The differences in vehicle composition between free-flow and congested extreme scenarios are compared under the same return period. The results show that truck percentage and clustering are decisive drivers; specific free-flow conditions can yield extreme effects comparable to, and occasionally no lower than, those under congestion. In addition, while specifically focusing on extreme scenarios, the proposed methodology substantially reduces computational cost and time, while preserving daily maxima, thereby supporting the development of real-time structural monitoring and early-warning systems under diverse traffic conditions.]]></description>
      <pubDate>Thu, 14 May 2026 14:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672341</guid>
    </item>
    <item>
      <title>A fundamental diagram-consistent fluid queue model for dynamic throughput under heavy traffic congestion</title>
      <link>https://trid.trb.org/View/2659379</link>
      <description><![CDATA[The efficient operation of transportation systems is a critical priority for policymakers, particularly given the increasingly emphasis on efficiency gains to meet growing travel demands without relying solely on capacity expansion. During peak hours at freeway bottleneck locations, a flow drop may be observed, and this drop is influenced by traffic stream attributes such as merging and diverging vehicles, resulting in significant efficiency losses. However, queue-based models, widely used for estimating delays and queue lengths, often oversimplify congestion dynamics by assuming a constant outflow rate, leading to inconsistencies when compared with FD-based observations of over-congested states. In this manuscript, we introduce a novel Fundamental Diagram-Consistent Fluid Queue (FDQ) framework for analyzing and mitigating traffic efficiency losses during heavy congestion. We extend the traditional fluid queue model by incorporating a stationary density-flow relationship observed empirically at key bottlenecks. Unlike classical queue-based models, our framework allows the flow throughput to evolve with local traffic state transitions, especially the shift from semi-congested to fully congested regimes. We start with triangular FD and show how to analytically derive FD-consistent dynamic flow throughput, as well as the associated traffic states such as the queue profile and waiting time. Such framework is then utilized to understand efficiency loss mechanisms and explore the potential for increased system efficiency through targeted inflow control. Two types of FDQ models were developed: one with flow throughput in polynomial form (FDQ-PN) and another in piecewise form (FDQ-PW). The FDQ framework is also extended to work with quadratic FD. Validation and numerical analyses were performed using datasets from Los Angeles I-405 and Phoenix I-10. The results demonstrate that the proposed framework substantially improves the accuracy of traffic state estimation. Furthermore, the demand–supply coupled inflow control is shown to offer a more significant efficiency gain than adjusting demand or supply alone.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659379</guid>
    </item>
    <item>
      <title>Quantification of Urban Resilience to Natural Hazards from Traffic-Flow Data</title>
      <link>https://trid.trb.org/View/2636054</link>
      <description><![CDATA[Given that in addition to the robustness of the transportation, utility, electricity, and telecommunication networks, perhaps the most robust network that serves urban resilience is the living network of the citizens of a city, in this paper we quantify urban resilience to natural hazards by processing traffic-flow data. From the recorded traffic-flow values (number of vehicles per time) at various locations on major traffic city arteries, we compute the probability density for finding a vehicle at some distance x, from the city center at time t, and, subsequently, we calculate the time history of the mean-square displacement (MSD) of vehicles from the city center. The shape of the MSD time histories computed in this study from traffic-flow data exhibits striking similarities with the MSD time histories computed by tracking GPS locations from individual cellphone users. Accordingly, this study that uses an entirely different type of data confirms our previous finding that large American cities, following a natural hazard, revert immediately to their initial steady-state behavior and resume their normal, pre-event activities.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636054</guid>
    </item>
    <item>
      <title>Optimal Real-Time Bidding Strategy for EV Aggregators in Wholesale Electricity Markets</title>
      <link>https://trid.trb.org/View/2553382</link>
      <description><![CDATA[With the rapid growth of electric vehicles (EVs), EV aggregators have been playing an increasingly vital role in power systems by not merely providing charging management but also participating in wholesale electricity markets. This work studies the optimal real-time bidding strategy for an EV aggregator. Since the charging process of EVs is time-coupled, it is necessary for EV aggregators to consider future operational conditions (e.g., future EV arrivals) when deciding the current bidding strategy. However, accurately forecasting future operational conditions is challenging under the inherent uncertainties. Hence, there demands a real-time bidding strategy based solely on the up-to-date information, which is the main goal of this work. We start by developing an online optimal EV charging management algorithm for the EV aggregator via Lyapunov optimization. Based on this, an optimal real-time bidding strategy (bidding function and bounds) for the aggregator is derived. Then, an efficient yet practical algorithm is proposed to obtain the bidding strategy. It shows that the cost of the aggregator is nearly offline optimal with the proposed bidding strategy. Moreover, the wholesale electricity market clearing result aligns with the individual aggregator’s optimal charging strategy given the prices. Case studies against several benchmarks are conducted to evaluate the performance of the proposed method.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553382</guid>
    </item>
    <item>
      <title>Modern Perspectives on Highway Geometric Design: Accounting for Driver Perception – A Review</title>
      <link>https://trid.trb.org/View/2659327</link>
      <description><![CDATA[Consistency in highway design is crucial for traffic safety and efficiency. In Vietnam, research on this remains limited. This paper evaluates modern design solutions based on international standards, focusing on design speed (VD) and operating speed (V85). The analysis shows that speed variation within traffic flow, rather than absolute speed, is the primary accident factor. Ensuring flow consistency helps reduce risks. Geometric road elements, including cross-section, alignment, and longitudinal profile, significantly affect accident frequency. Solutions such as optimal lane width, large curve radii, and auxiliary or emergency lanes enhance safety. The findings highlight the need for domestic research, updated design standards aligned with modern traffic conditions, and lessons from international practices to reduce accidents and improve road user safety in Vietnam.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659327</guid>
    </item>
    <item>
      <title>A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models</title>
      <link>https://trid.trb.org/View/2642460</link>
      <description><![CDATA[Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific datasets limits generalization across diverse scenarios and reduces reliability in real-world deployment. In addition to behavioral fidelity, ensuring traffic stability is increasingly critical for the safe and efficient operation of autonomous vehicles (AVs), requiring CFMs that jointly address both objectives. However, existing models generally do not support a systematic integration of these goals. To bridge this gap, we propose a knowledge-informed deep learning (KIDL) paradigm that distills the generalization capabilities of pre-trained large language models (LLMs) into a lightweight and stability-aware neural architecture. LLMs are used to extract fundamental car-following knowledge beyond dataset-specific patterns, and this knowledge is transferred to a reliable, tractable, and computationally efficient model through knowledge distillation. KIDL also incorporates stability constraints directly into its training objective, ensuring that the resulting model not only emulates human-like behavior but also satisfies the local and string stability requirements essential for real-world AV deployment. We evaluate KIDL on the real-world NGSIM and HighD datasets, comparing its performance with representative physics-based, data-driven, and hybrid CFMs. Both empirical and theoretical results consistently demonstrate KIDL’s superior behavioral generalization and traffic flow stability, offering a robust and scalable solution for next-generation traffic systems.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642460</guid>
    </item>
    <item>
      <title>The effect of license plate number-based vehicle restrictions on crash frequency</title>
      <link>https://trid.trb.org/View/2630623</link>
      <description><![CDATA[This study investigates the effects of license plate-based vehicular circulation restrictions, known as “peak-and-plate” policies, on urban traffic crashes in Medellín, Colombia. Although such policies are widely implemented to reduce congestion and emissions, limited research has examined their unintended safety impacts on crash frequency before and during the restriction periods. Considering high-resolution crash data from 2008 to 2022, this study evaluates the temporal effects of these restrictions on crash frequency across different hours, months, and weekdays. Statistical preprocessing and hierarchical clustering techniques were applied to discover crash temporal patterns, and negative binomial, pooled, fixed effects, and random effects models were tested to account for overdispersion and panel structure in the data. Results reveal that crash frequencies increased consistently in the 30 min preceding the enforcement of circulation restrictions, particularly during the morning hours. Surprisingly, crash rates remained elevated during the restriction period, contrary to expectations that reduced vehicle volume would lower crash occurrences. These findings suggest the presence of confounding behavioral, spatial, or enforcement-related factors. Additionally, monthly and weekly crash patterns correlate with seasonal variations, holidays, and changes in traffic volume, emphasizing the importance of context-aware traffic policies. Statistical evidence suggests that current vehicle restriction policies may inadvertently concentrate risk in specific time windows. These insights highlight the need for policymakers to refine urban mobility strategies by considering crash and traffic data simultaneously, implementing targeted public awareness campaigns, targeting speed enforcement, and incorporating enforcement dynamics into the design of vehicle restriction programs.]]></description>
      <pubDate>Tue, 23 Dec 2025 09:29:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630623</guid>
    </item>
    <item>
      <title>Traffic Signal Operations Supporting All Users</title>
      <link>https://trid.trb.org/View/2604095</link>
      <description><![CDATA[Common complete street traffic signal timing strategies include leading pedestrian intervals, exclusive bicycle and pedestrian phasing, transit and bicycle queue jumps, and more. These countermeasures are utilized to curb pedestrian and bicycle crashes with vehicles, however, they often come at the expense of decreased travel efficiency. Safety and congestion management can sometimes conflict with each other. The objective of this document is to provide the Texas Department of Transportation (TxDOT) with a catalogue of commonly used operation strategies including safety benefits and applicability to TxDOT roadways.]]></description>
      <pubDate>Thu, 02 Oct 2025 11:36:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604095</guid>
    </item>
    <item>
      <title>Network-level analysis of the effects of drivers’ preferences toward driving behavior of Automated Vehicles: a microsimulation study on a highway segment</title>
      <link>https://trid.trb.org/View/2571067</link>
      <description><![CDATA[With the increasing prevalence of Level 2 and Level 3 Automated Vehicles (AVs) equipped with advanced technological features like Adaptive Cruise Control (ACC), the need to investigate their impact on traffic efficiency when sharing roads with Human-Driven Vehicles (HDVs) becomes crucial. The performance of these AVs (Level 2 and Level 3) primarily depends on the behaviour of users, as they prefer a similar or more defensive driving style when riding in AVs compared to their normal driving behaviour. Neglecting this diversity in the driving styles of AVs undermines the accuracy of the results of studies investigating their impact on traffic efficiency. Therefore, this study aimed to assess the impact of AVs on travel time, delay, and flow rate, considering the typical driving styles of drivers. The authors investigated a gradual increase in the penetration rate of AVs from 0% to 100% in 25% increments within a simulated mixed traffic environment of AVs and HDVs on a highway segment in the Veneto region, Italy, using the VISSIM microsimulation software. The results show that an increase in the penetration rates of AVs with different driving styles increases travel time (1.08% - 4.09%) and delay (13.22% - 99.28%) while exhibiting a negligible improvement in flow rate (0.09% - 0.26%).]]></description>
      <pubDate>Thu, 21 Aug 2025 16:30:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571067</guid>
    </item>
    <item>
      <title>Analysing Headway Spacing and Calculating Passenger Car Equivalent Values Using Computer Vision and International Dataset</title>
      <link>https://trid.trb.org/View/2577218</link>
      <description><![CDATA[Accurate traffic flow data are crucial for effective transportation planning and management. Different vehicle types impact traffic flow variably, requiring distinct passenger car equivalency (PCE) factors for calculating intersection and road capacity. Headway and spacing data are essential to assess traffic density and service level. Conventional data collection methods are time-consuming and often inaccurate. Unlike existing studies, this study employed computer vision to measure mixed traffic stream volume in terms of passenger car equivalent and collect headway-spacing data with high accuracy. The vehicle detection and counting procedures provide the mandatory infrastructure for measuring mixed traffic stream volume and collecting headway and spacing data. Novel approaches were introduced to gather comprehensive traffic data, including passenger car equivalent values, headway, spacing, flow rate, vehicle speed and traffic volume, using a single system. A custom and comprehensive international dataset was collected to analyse these approaches. The authors' trained model achieved a mean average precision (mAP) of 97.4%, with accuracies of 95% for headway, 93% for spacing and 99% for PCE values. The dataset can be downloaded at https://github.com/burak-celik/atavehicledataset.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577218</guid>
    </item>
    <item>
      <title>Estimating real-time traffic state of holding vehicles at signalized intersections using partial connected vehicle trajectory data</title>
      <link>https://trid.trb.org/View/2569852</link>
      <description><![CDATA[Emerging connected vehicle (CV) technologies offer unprecedented opportunities to estimate various traffic states, enhancing traffic management and control. Among these states, a particularly critical yet underexplored one is the number of holding vehicles—vehicles that, based on their projected trajectories using cruise speed, should have been discharged at any instant of interest but are instead impeded and remain undischarged. Accurately estimating this quantity is essential for real-time traffic state monitoring and control, as it directly reflects the effectiveness of traffic flow at intersections. However, the prolonged transition period implies a mix of CVs and non-connected vehicles (NCs) within transportation networks, resulting in incomplete traffic information. To address this challenge, this paper proposes a generic and fully analytical CV-based holding vehicle (CVHV) model to estimate the number of holding vehicles at any instant of interest, relying solely on partial CV trajectory data. The CVHV model accommodates any signal plans, CV penetration rates, and traffic demands. Two sub-models, CVHV-I and CVHV-II, are derived to account for different holding vehicle patterns at any instant of interest falling within the effective red or green of a signal group, respectively. Each sub-model handles various holding vehicle patterns, including holding vehicle components such as stopped holding CVs and NCs, and moving holding CVs and NCs. Comprehensive numerical experiments in VISSIM validate the effectiveness of the CVHV model under varying volume-to-capacity ratios, CV penetration rates, and signal timing configurations. Its practical applicability is further demonstrated using the real-world Next Generation Simulation dataset. Additionally, the application of the proposed model to estimating the real-time total number of vehicles in a lane and to a simple illustrative example of CV-based adaptive signal control highlights the significance of accurately estimating the traffic state of holding vehicles.]]></description>
      <pubDate>Wed, 16 Jul 2025 19:48:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569852</guid>
    </item>
    <item>
      <title>Estimation of Passenger Car Equivalents on Basic Freeway Segments from Field-Observed Traffic Data</title>
      <link>https://trid.trb.org/View/2573092</link>
      <description><![CDATA[Passenger car equivalents (PCEs) represent the effects of heavy vehicles on traffic operations. PCE estimation is based on equating the passenger-car-only flow rate to the mix-fleet flow rate such that it results in the same performance for the selected measure. Most existing methods rely on simulation to generate PCE values. However, this approach generates PCEs using the truck characteristics of the simulation rather than local field conditions and heavy vehicle types. Most existing methods estimate the marginal effect of adding one specific truck in the traffic stream assuming relatively high volumes, which results in higher PCE values. This study proposes a new method for estimating PCEs using field-observed traffic data considering the impact of each truck type for a broad set of flows, not just their marginal effect at high flows. Based on density equivalency, the maximum likelihood estimation is used to estimate the means and the variances of the PCE values from traffic data collected at 10 sites on the national motorways of Thailand. When aggregated across all observed percentages of heavy vehicles and flow rates, the PCE values are 1.46 (trucks with length between 5.2 and 13.0?m) and 2.08 (trucks with length of 13.1?m or longer). For low to medium percentage of trucks, the PCEs become lower after the onset of oversaturation. However, for high percentages of trucks the PCEs tend to be higher after the onset of oversaturation. The proposed methodology can be applied using data from other locations to estimate the corresponding PCEs from field-observed data.]]></description>
      <pubDate>Tue, 15 Jul 2025 09:47:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573092</guid>
    </item>
    <item>
      <title>Ranking dynamics of urban mobility</title>
      <link>https://trid.trb.org/View/2569947</link>
      <description><![CDATA[Human mobility, a pivotal aspect of urban dynamics, displays a profound and multifaceted relationship with urban sustainability. Despite considerable efforts analyzing mobility patterns over decades, the ranking dynamics of urban mobility has received limited attention. This study aims to contribute to the field by investigating changes in rank and size of hourly inflows to various locations across 60 Chinese cities throughout the day. The authors find that the rank–size distribution of hourly inflows over the course of the day is stable across cities. To uncover the microdynamics beneath the stable aggregate distribution amidst shifting location inflows, they analyzed consecutive-hour inflow size and ranking variations. Their findings reveal a dichotomy: locations with higher daily average inflow display a clear monotonic trend, with more pronounced increases or decreases in consecutive-hour inflow. In contrast, ranking variations exhibit a non-monotonic pattern, characterized by the stability not only of the top and bottom rankings, but also of mid-ranked locations in certain cities. Finally, they compare ranking dynamics across land use types and cities. The results advance their understanding of urban mobility dynamics, providing a basis for applications in urban planning and traffic engineering.]]></description>
      <pubDate>Thu, 10 Jul 2025 16:40:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569947</guid>
    </item>
    <item>
      <title>The influence of the road on the occurrence of road accidents in the work zone on state roads in the Republic of Serbia for the period from 2018 to 2022</title>
      <link>https://trid.trb.org/View/2528582</link>
      <description><![CDATA[At the beginning of the development of road safety, it was considered that man, vehicle and environment are the only factors that affect road safety. Very quickly there was a development and the realization that it is necessary to emphasize the road as a special factor. Today, the basic factors of road safety are man, vehicle, road and environment. According to world experience, the road itself or in combination with other factors is the cause of approximately one third of all road accidents. By marking the work zone on the road, there is a change in the normal mode of traffic flow, in order to protect the participants in the traffic as well as the construction site itself and the contractors. Some of the ways to increase the level of safety in the work zone are related to the creation of narrowing when moving, reducing the speed of movement, installing light traffic signals, etc. In addition to all the applied traffic and technical measures, road accidents still occur in the work zone. The focus of this study lies in analysing road accidents occurring within construction zones on state roads in the Republic of Serbia between 2018 and 2022. Additionally, it aims to illustrate the influence of road-related factors on the incidence of these accidents. The primary objective is to present the findings derived from this analysis and propose measures aimed at enhancing safety levels within road work zones. Furthermore, the paper seeks to suggest avenues for future research in this domain.]]></description>
      <pubDate>Thu, 08 May 2025 14:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2528582</guid>
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