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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>Investigating the formation mechanism of merging/diverging collision risk in short weaving segments: An integrated approach using spatial-temporal risk field and explainable machine learning</title>
      <link>https://trid.trb.org/View/2693795</link>
      <description><![CDATA[Merging and diverging operations in short weaving segments on urban expressways are high-risk scenarios that frequently lead to collisions. Investigating the formation mechanism of merging/diverging collision risk is essential for developing effective control strategies. However, the scarcity of micro-level collision risk events and the unclear correlation between macro- and micro-level risks hinder the integration of risk identification and control strategies. To address these challenges, we integrate the spatial–temporal risk field (STRF) with explainable machine learning to construct a systematic framework for investigating the formation mechanism of collision risks in short weaving segments. Using seven representative weaving segments with distinct configurations on the eastern and western expressways of Changchun, we collected 3.6 h of aerial video data and extracted vehicle trajectories through an improved YOLO algorithm. Based on STRF, we developed a micro-level collision risk event identification model that simultaneously captures rear-end, lateral, and environmental risks. STRF clearly demonstrates the risk distribution characteristics of merging and diverging in different weaving segments. We then built a three-dimensional macro-explanatory variable system encompassing lane-, segment-, and factor-level attributes. Through a three-stage refinement process, the optimized XGBoost model achieved superior performance across 14 training tasks, consistently outperforming all baseline models. By integrating SHAP interpretability analysis, we revealed both the commonalities and heterogeneities in how macro-level factors influence micro-level risks under varying movement patterns and weaving segment configurations. Finally, we proposed targeted macro-level control strategies tailored to distinct traffic flow characteristics, geometric layouts, and spatial distributions of risk points across different types of short weaving segments.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693795</guid>
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    <item>
      <title>Vehicle Trajectory Prediction in Highway Weaving Areas Considering Interaction-Transfer Characteristics</title>
      <link>https://trid.trb.org/View/2717674</link>
      <description><![CDATA[In order to enhance safety in dynamic traffic, autonomous vehicles must predict surrounding vehicles' trajectories to reduce accident risk. Addressing the issue of insufficient consideration of vehicle interaction transfer characteristics in existing studies, this paper proposes a trajectory prediction framework for highway weaving areas that explicitly accounts for vehicle interaction transfer effects. This method uses a hierarchical dynamic graph to comprehensively represent interaction transfer characteristics and achieves cross-layer coupling at both the feature level and the decision level, which helps to address the complexity of vehicle interactions in mixed traffic environments. The proposed graph attention network for trajectory prediction, which models the transfer characteristics of vehicle interactions, fully exploits temporal continuity and spatial coupling to jointly extract spatiotemporal features. This paper infers latent interaction information transmitted by indirectly adjacent vehicles based on vehicle historical data. The inferred information is integrated with vehicle state features and direct interaction information. A hierarchical dynamic graph is then constructed to account for interaction transfer among vehicles. To overcome insufficient modeling of coupled spatiotemporal dependencies, the authors design a spatiotemporal dynamic graph attention network model for trajectory prediction. Experiments were conducted on publicly available datasets. The results indicate that the model's predictions surpass those of the currently advanced baseline models, demonstrating higher prediction accuracy.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717674</guid>
    </item>
    <item>
      <title>Causal inference-based research on decision characteristics and active control of overtaking lane-changing behavior in expressway interchange weaving areas</title>
      <link>https://trid.trb.org/View/2672699</link>
      <description><![CDATA[The urban expressway network is a critical infrastructure for modern transportation systems, with the weaving area being a key component. However, the complex lane-changing behaviors in these areas, particularly overtaking lane-changing (OLC), have become a primary bottleneck affecting traffic safety and efficiency. Existing studies predominantly focus on merging/diverging zones or descriptive analyses, lacking causal insights into driver decision-making under dynamic conditions. This gap limits proactive safety measures. Our study addresses this by employing causal inference and counterfactual estimation to quantify how driver perceptions, traffic density, and inter-vehicle distance influence OLC acceptance. Integrating machine learning, we propose actionable strategies for real-time OLC control. Through a decision-making experiment, we collected the decision data on OLC behavior. Average treatment effect and conditional average treatment effect are calculated to identify key indicators influencing driver decisions during OLC maneuvers. Combined with machine learning techniques, we also developed a counterfactual estimation model to estimate overtaking acceptance under specific interventions. The estimation results can be used to support active control of OLC behavior. The findings from this research provide a deeper understanding of the intricate relationships and mechanisms underlying OLC behavior. The predictive model offers a significant advancement, enabling more accurate evaluation of current systems and informing the development of more effective transportation solutions. The study’s implications are crucial for optimizing traffic flow and safety within urban expressway interchange weaving areas.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:35:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672699</guid>
    </item>
    <item>
      <title>Highway Safety Manual Crash Prediction Models of Arterial Weaving Segments</title>
      <link>https://trid.trb.org/View/2712176</link>
      <description><![CDATA[More than half of U.S. roadway deaths and nearly two-thirds of pedestrian fatalities occur on non-freeway arterials. Arterial sections with weaving maneuvers are complex for all road users to navigate and traverse without incidents or collisions.

The Code of Federal Regulations requires determination of whether the location, configuration, geometric design, and signing related to a proposed change in access may be reasonably expected to serve the anticipated traffic of the Interstate system in a manner that is conducive to safety, durability, and economy of maintenance. For many existing and proposed alternative designs, the safety of the weave is not quantified between ramps. Examples include cloverleaf designs with adjacent intersections and crossing weaves from ramps to downstream left turns. A better understanding of crash outcomes is needed for a variety of rural and urban speeds and contexts.

As part of NCHRP Project 15-66, “Operational Performance and Safety Effects of Arterial Weaving Sections,” crash data and conflict data obtained in the field and a driving simulator were analyzed to assess the safety performance of several types of arterial weaving sections. The results of the safety analysis did not provide a definitive relationship between the length and vehicle maneuvers of arterial weaving sections and crashes or conflicts; however, sufficient information was found to suggest additional research in this area would yield promising results toward developing a methodology for predicting the safety performance of arterial weaving sections suitable for inclusion in the AASHTO Highway Safety Manual (HSM).

The objective of this research is to develop a crash prediction methodology, safety performance functions (SPFs), to assess different types of arterial weaving sections, suitable for inclusion in the HSM.]]></description>
      <pubDate>Tue, 09 Jun 2026 14:53:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712176</guid>
    </item>
    <item>
      <title>A novel data-driven theory for interactive behavior and risk assessment modelling in urban expressway weaving sections: Evidence from UAV data and transformer models</title>
      <link>https://trid.trb.org/View/2673143</link>
      <description><![CDATA[With the increasing complexity of traffic behaviors in urban expressway weaving sections, traditional dynamic models exhibit limitations in quantifying driver state changes, capturing multi-layer environmental influences, and predicting interaction risks spanning macro and micro scales. To address these gaps, this study proposes an innovative interaction behavior modeling and risk assessment method that integrates energy field theory, high-precision traffic data, and the Transformer architecture. First, leveraging high-precision data collected from real-world scenarios, we quantify diverse environmental elements in weaving sections (including facility, rule, and kinematic layers) into unified energy values, constructing a three-layer energy field model that synergizes gravitational field and electromagnetic field theories. This model enables consistent characterization of dynamic energy transfer between vehicles and their surroundings. On this basis, we define three typical weaving behaviors and two integrated weaving patterns, then establish an energy field-based interaction model that quantifies core metrics such as interaction probability, degree, and duration using methods including the Weibull function and information entropy. To achieve accurate risk assessment covering both microscopic vehicle-to-vehicle interactions and macroscopic section-wide safety, we further adopt the self-attention mechanism to capture complex spatiotemporal dependencies among vehicles, realizing precise prediction of local and global risks. Validated using real-world datasets from two case studies, the proposed framework clearly elucidates the evolutionary mechanisms and distinctions between the two integrated weaving patterns. In terms of prediction performance, the Transformer model outperforms LSTM, GRU, BiLSTM, and BiGRU across all evaluation metrics (MSE, MAE, RMSE, and Miss Rate), demonstrating superior precision in local risk prediction and robustness in global risk assessment. This study not only provides a unified theoretical framework for synergizing energy field theory and advanced deep learning to analyze complex weaving behaviors, but also offers a feasible technical method for real-time risk prediction in intelligent transportation management systems.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673143</guid>
    </item>
    <item>
      <title>Traffic conflict characteristics and evolution mechanism in freeway weaving segments with varying spacing</title>
      <link>https://trid.trb.org/View/2701556</link>
      <description><![CDATA[With the rapid expansion of China’s freeway network and the increasing density of interchange clusters, reduced interchange spacing has emerged as a critical challenge for weaving section safety. To systematically characterize how spacing affects the distribution, severity, and duration of traffic conflicts in weaving areas, this study collected aerial video data from four weaving sections with spacing of 400 m, 600 m, 850 m, and 1400 m along the Changhu Freeway in Dongguan. Using the Data from Sky video analysis platform for trajectory extraction and time-to-collision (TTC)-based surrogate safety analysis, the spatiotemporal evolution of traffic conflicts under varying spacing conditions was investigated. The research demonstrates that spacing is a critical factor influencing conflict risk. Reduced spacing exacerbates risk through spatial compression effects, manifested by a leftward shift of the primary TTC distribution peak, reduced collision avoidance time, and a significant increase in both the proportion of critical conflicts and TET duration. In contrast, longer spacing (850 m and 1400 m) provides necessary buffer space, optimizes conflict distribution, and reduces severity. The weaving flow ratio shows a positive correlation with the unit conflict rate. The 850 m scenario exhibits the highest conflict rate due to its combination of high traffic volume and high weaving flow ratio; however, conflict severity remains primarily regulated by spacing. Conflicts between heavy vehicles (L-L) present the highest risk, with a mean TTC of only 2.3 s and a critical conflict proportion of 18.53%, while light vehicle combinations (S-S) perform optimally. Vehicle movement intentions also significantly influence conflict characteristics: combinations with shared intentions (S-S, F-F, H-H) exhibit elevated risks due to high trajectory overlap, and their conflict numbers show an increasing trend under longer spacing. In contrast, heterogeneous vehicle combinations (S-F, S-H) are less affected by spacing variations. Based on these findings, a minimum spacing of 850 m is recommended as a practical safety threshold for weaving section design, and vehicle-type-specific management measures are advised for constrained sections below this benchmark. The findings of this study can provide a theoretical foundation and practical guidance for the planning, design, and management of weaving sections in freeway interchanges.]]></description>
      <pubDate>Wed, 27 May 2026 10:48:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701556</guid>
    </item>
    <item>
      <title>Spatial-temporal risk field-based coupled dynamic-static driving risk assessment and trajectory planning in weaving segments</title>
      <link>https://trid.trb.org/View/2694634</link>
      <description><![CDATA[As connected and automated vehicles (CAVs) gradually penetrate the existing transportation system, the inherent turbulence within weaving segments is expected to be mitigated through CAV technologies. However, despite making progress in capturing static and dynamic risk factors, traditional CAV technologies still lack foreseeability for dynamic risks. This leads to suboptimal results in trajectory planning, thereby hindering the maximization of expected benefits. To fill these gaps, the authors first propose a spatial–temporal coupled risk assessment paradigm by constructing a three-dimensional spatial–temporal risk field (STRF). Specifically, the authors introduce spatial–temporal distances to quantify the impact of future trajectories of dynamic obstacles. The authors also incorporate a geometrically configured specialized field for weaving segments to constrain vehicle movement directionally. To enhance the STRF’s accuracy, the authors further developed a parameter calibration method using real-world aerial video data, leveraging YOLO-based machine vision and dynamic risk balance theory. A comparative analysis with traditional risk field shows that the STRF possesses superior risk foreseeability. Building on these results, the authors final design a STRF-based CAV trajectory planning method in weaving segments. The authors integrate spatial–temporal risk occupancy maps, dynamic iterative sampling, and quadratic programming to enhance safety, comfort, and efficiency. By incorporating both dynamic and static risk factors during the sampling phase, the method ensures robust safety performance. Additionally, the proposed method simultaneously optimizes path and speed using a parallel computing approach, reducing computation time. Real-world cases show that, compared to the baseline schemes, and real human driving trajectories, the method significantly improves safety, reduces lane-change completion time, and minimizes speed fluctuations.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694634</guid>
    </item>
    <item>
      <title>Reinforcement learning control in complex freeway weaving sections under mixed traffic and platoon formation</title>
      <link>https://trid.trb.org/View/2663292</link>
      <description><![CDATA[Ramp Metering (RM) and Variable Speed Limits (VSL) are two widely studied and implemented ITS methods that can improve freeway traffic conditions under high traffic flows. In this work, a novel reinforcement learning (RL) control approach is introduced, which integrates RM and VSL within mixed traffic conditions, while allowing for the spontaneous formation of connected autonomous vehicle (CAV) platoons. The RL agent is trained in a microscopic simulation environment representing a freeway weaving section, under diverse traffic demands and CAV market penetration rates. The results indicate the superiority of the proposed method compared to feedback control (ALINEA) and no control, reducing the cumulative Total Time Spent by up to 13.49% and 40.55% respectively. Finally, as the CAV market penetration rate increases, VSL becomes more effective than RM, and the agent’s margin of improvement decreases due to the capacity gain from the platoon formation.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663292</guid>
    </item>
    <item>
      <title>Systematic review of weaving area safety: Assessment, behavior, and countermeasures</title>
      <link>https://trid.trb.org/View/2676432</link>
      <description><![CDATA[Ensuring safety in road weaving areas remains a critical challenge for modern highway networks due to their inherent operational complexity. These areas serve as vital nodes for traffic exchange but are characterized by intense mandatory lane changes and traffic turbulence, making them persistent hotspots for crashes and congestion. To consolidate the vast and evolving body of research on this topic, this study conducts a systematic review of road weaving area safety in accordance with the PRISMA methodology. Based on a final corpus of 83 studies published between 2004 and 2025, the literature is synthesized using a four-part thematic framework: Crash Analysis, Traffic Conflict Analysis, Driving Behavior Analysis, and Safety Improvement Strategies and Interventions. The synthesis reveals that weaving area safety is an emergent property of a complex system, governed by a causal feedback loop linking static geometry, dynamic traffic flow, and microscopic driver behavior. A dominant behavioral pattern identified is the tendency for drivers to front-load mandatory lane changes, concentrating turbulence at the segment entrance and leading to underutilization of downstream infrastructure. The review traces a clear evolution in the research paradigm from reactive, crash-based analysis to proactive, conflict-based prediction. This shift has been enabled by advancements in data acquisition and analytical methods, which have fundamentally redefined how risk is conceptualized and measured. Correspondingly, safety interventions have progressed through a clear hierarchy of control, from static geometric design, through reactive active traffic management and proactive cooperative intelligent transport systems advisories, to fully cooperative systems for Connected and Automated Vehicles (CAVs). Finally, the study outlines critical future research directions, highlighting the need for human-centered risk modeling, the validation of surrogate safety measures in mixed-autonomy environments, and the development and testing of robust cooperative control strategies for CAVs.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676432</guid>
    </item>
    <item>
      <title>Evaluation Methods for Urban Expressway Weaving Sections Based on a Risk Index Model: A Driving Simulation Study</title>
      <link>https://trid.trb.org/View/2613356</link>
      <description><![CDATA[Urban expressway weaving sections, characterized by complex vehicle interactions such as merging, diverging, and lane changing, are critical areas in traffic safety research. This study employs a driving simulation experiment to design scenarios with varying weaving section lengths, traffic flow densities, and driving paths. A risk index model is developed to quantitatively assess the operational risks in these sections. The results indicate that weaving section length, traffic flow density, and driving paths significantly influence risk levels. The proposed risk classification method provides a scientific basis for the safety management of weaving sections.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613356</guid>
    </item>
    <item>
      <title>Development of a New Methodology for Evaluating Arterial Weaving Sections Including All Traffic Movements</title>
      <link>https://trid.trb.org/View/2663313</link>
      <description><![CDATA[Arterial weaving segments present unique operational challenges because of frequent lane changes between origin–destination (OD) pairs. Existing methodologies, such as those in the Highway Capacity Manual (HCM7) and NCHRP 15-66, are formulated exclusively for through movements and therefore cannot accurately predict running speeds for vehicles performing weaving or turning maneuvers, which may represent more than half of the total traffic in these facilities. This study develops and validates a new methodology that estimates running speeds by OD and by segment, explicitly capturing the effects of lane utilization, turbulence index, and weaving ratio. Using the NCHRP 15-66 dataset, which includes drone observations from fifteen field sites and more than 1,000 calibrated microsimulation runs, models were estimated using feasible generalized least squares and evaluated through 10-fold cross-validation. The analysis confirmed that vehicles associated with different ODs exhibit statistically distinct running speeds, reinforcing the need for OD-specific modeling. The proposed formulation achieved an average RMSE of 5.27 mph across all OD movements and produced 29.6% lower RMSE than NCHRP 15-66 for through movements under high-demand conditions (> 450 vehicles per hour per lane). These findings demonstrate that OD-specific and regime-based modeling provides a more accurate and transferable representation of arterial weaving operations than existing analytical approaches.]]></description>
      <pubDate>Wed, 04 Feb 2026 16:29:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663313</guid>
    </item>
    <item>
      <title>Meta-MSCC: A foundation model for adaptive CAV control in highway weaving segments</title>
      <link>https://trid.trb.org/View/2614788</link>
      <description><![CDATA[The connected and automated vehicle (CAV) technology offers new opportunities for active traffic management in highway weaving segments. Current CAV-based control strategies have not fully accounted for their coupling impacts on safety and efficiency in highway weaving segments. Moreover, most existing active traffic control models still have limitations in adaptability and transferability across diverse weaving scenarios. To address these issues, this study proposes a novel multi-strategy cooperative control model based on meta-deep reinforcement learning algorithm, named Meta-MSCC, which serves as a lightweight domain-specific foundation model for optimizing both safety and efficiency in highway weaving segments under mixed connected automated traffic. A total of 22,306 real-world vehicle trajectories, including 1,488 near-crash events, are used to determine an acceptable collision risk level as the safety benchmark. Additionally, 20 high-risk and congested weaving scenarios are constructed through microscopic simulations to train and validate the Meta-MSCC. This study further develops a multi-level CAV-based control strategy by integrating microscopic (trajectory), mesoscopic (speed), and macroscopic (flow) control approaches. Then, a multi-strategy cooperative control model is put forward based on the actor-critic framework, which can effectively capture coupling impacts of this strategy on safety and efficiency. After incorporating a meta-learning algorithm (i.e., meta-deep deterministic policy gradient) into the actor-critic framework, the Meta-MSCC is finally formed as a lightweight domain-specific foundation model, enhancing adaptability and transferability across diverse weaving scenarios. The results show that the multi-level CAV-based control strategy achieves greater improvements in safety and efficiency than single-level strategies. Furthermore, the proposed Meta-MSCC outperforms other classical DRL-based models, with collision risk reduced by 49.1% and time delay by 12.6%, while maintaining stable optimization performance at CAV penetration rates exceeding 60%. The findings of this study might contribute to the enhancement of active traffic management and the optimization of intelligent road infrastructures.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614788</guid>
    </item>
    <item>
      <title>A review of capacity modelling techniques for large multi-lane roundabouts with weaving</title>
      <link>https://trid.trb.org/View/2652411</link>
      <description><![CDATA[Multi-lane characteristics in large roundabouts promote driver lane choices, causing an interaction between entry and circulating flows called weaving. Weaving affects roundabout capacity significantly as the entry rate reduces when vehicles interact with more conflicting flows. Thus, the inclusion of weaving in the capacity analysis is essential. This paper first discusses large roundabouts and performs a literature review on existing roundabout capacity modelling techniques. From there, the paper investigates current techniques to estimate the capacity for large multi-lane roundabouts from viewpoints of their capability in evaluating the capacity of large roundabouts, limitations, possible applications of simulation and machine learning in modelling large roundabouts, and modifications from the freeway weaving section. This review shows that current techniques are inappropriate for large multi-lane roundabouts with weaving for two reasons: (1) techniques mainly were introduced and applied for roundabouts with inscribed circle diameters of less than 100 m; (2) techniques adopt multi-lane circulating streams as a total flow. This simplification cannot capture possible weaving between two streams, which significantly impacts the capacity of large roundabouts due to free driver lane choices. The research identified a challenge: the absence of a capacity modelling technique for large multi-lane roundabouts considering weaving, although many large multi-lane roundabouts are available worldwide. Research should continue to propose a method to assess the capacity of large multi-lane roundabouts considering weaving.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652411</guid>
    </item>
    <item>
      <title>Incorporating Roadside Traffic Data to Predict Lane Change Behaviors within Weaving Segment for Automated Vehicles</title>
      <link>https://trid.trb.org/View/2608138</link>
      <description><![CDATA[The weaving segment is the bottleneck of expressways, as onramp and offramp traffic intermingles. While numerous studies have focused on predicting lane change behaviors within these segments, many existing approaches fail to exploit the synergistic potential of multisource perception data. This study proposes a novel two-stage prediction model that integrates onboard and roadside perception for enhanced lane change prediction within weaving segments. The proposed methodology comprises sequential intention recognition and trajectory prediction stages. In the first stage, the model leverages roadside perception to assist in inferring early lane change intentions through latent traffic information. In the second stage, it predicts precise lane-changing trajectories with the predicted lane change intentions as prior knowledge. The model’s performance was validated using field trajectory data. Specifically, the F1-score is improved by 4.77% for the right lane change intention category. The average displacement error (ADE) and final displacement error (FDE) of predicted trajectories are reduced by 15.3% and 10.1%, respectively. This study provides a fresh perspective on lane change prediction within weaving segments and achieves inspiring results.]]></description>
      <pubDate>Mon, 15 Dec 2025 10:34:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608138</guid>
    </item>
    <item>
      <title>An enhanced approach for weaving area capacity estimation combining high-fidelity simulation and interpretable machine learning</title>
      <link>https://trid.trb.org/View/2608473</link>
      <description><![CDATA[Accurately assessing weaving area capacity is critical for optimizing traffic management. However, transportation agencies face a persistent challenge: Existing capacity models often inadequately characterize vehicle weaving behaviors and fail to quantify the interactions between influencing factors. This limitation hinders the development of precise traffic control strategies in weaving areas. Addressing this, we propose an integrated methodology combining enhanced microscopic simulation with interpretable machine learning. The proposed method is tested and validated on two datasets. The DIEGA calibration-improved method integrates density-based spatial clustering of applications with noise (DBSCAN) clustering, information entropy, and genetic algorithms (GA) to achieve superior modeling accuracy and 22.2% faster convergence than GA. Simulation experiments demonstrate that under a constant weaving flow of 1900 pcu/h, variations in ramp-to-freeway (QRF)/freeway-to-ramp (QFR) ratios induce a capacity fluctuation of approximately 15% (ranging from 4277 to 4937 pcu/h) and indicate nonlinear coupling among QRF, QFR, weaving length (LW), and capacity. The ML_RF capacity model outperforms the baseline model while providing sHapley additive exPlanations (SHAP)-based interpretability of factor interactions. The methodology's demonstrated capability to identify optimal capacity ranges (4635–4860 pcu/h at QRF/ QFR≈ 1 with  LW= 250–350 m) provides transportation agencies with a potential decision-support tool for both weaving area design optimization and operational traffic management.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:23:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608473</guid>
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