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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>An explicit approach for modeling the performance of transportation networks immediately after an earthquake</title>
      <link>https://trid.trb.org/View/2691683</link>
      <description><![CDATA[Surface transportation systems play a vital role in supporting a region’s functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691683</guid>
    </item>
    <item>
      <title>Leveraging connected vehicle data for near-crash detection and analysis in urban environments</title>
      <link>https://trid.trb.org/View/2692440</link>
      <description><![CDATA[Urban traffic safety is a pressing concern in modern transportation systems, especially in rapidly expanding metropolitan areas where traffic congestion and diverse driving behavior increase the risk of traffic incidents. As situations in which vehicles come close to colliding while in motion (i.e., drivers took rapid evasive action to avoid an actual crash), near-crash events offer more sensitive insight into underlying roadway safety risks than traditional crash data. This study develops a framework integrating spatial-temporal buffering, heading algorithms, and a binary logistic regression model to identify and analyze near-crash events across San Antonio, Texas, revealing key environmental and traffic factors. Results show that over half of near-crash events involve vehicles traveling over 57.98 mph, with distances less than 20 m between them. Additionally, road segments carrying higher proportions of large or less maneuverable vehicles exhibit elevated near-crash likelihood, and this pattern is amplified on high-level roads, where speed and flow complexity elevate conflict risk. Near-crash risks are highest during weekday peak hours, particularly in downtown freeways and high-income neighborhoods. Findings suggest that strategies such as speed management, optimized routes, real-time monitoring, and adaptive traffic control based on data-driven insights can help reduce urban near-crash risks and enhance metropolitan traffic safety.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692440</guid>
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    <item>
      <title>Optimal vehicle fleet and charging strategy for electric ground service equipment at airports based on flight schedule</title>
      <link>https://trid.trb.org/View/2687381</link>
      <description><![CDATA[As the aviation industry transitions towards a green and low-carbon future, deploying electric ground service equipment (E-GSE) and integrating photovoltaic (PV) generation at airports can reduce energy consumption and emissions. This study proposes a two-stage optimization framework. In the first stage, a long short-term memory (LSTM) neural network is employed to forecast seasonal PV power generation, aiming to mitigate the impact of PV output volatility on downstream decision-making. In the second stage, a techno-economic optimization model is formulated, incorporating vehicle operation time constraints, time-of-use electricity pricing, and PV generation, to minimize the annual total cost for airport ground operation. The framework simultaneously optimizes vehicle procurement, charger allocation, and charging schedules. To enhance computational efficiency, the model is linearized, and a Benders decomposition-based mixed-integer programming approach is introduced to solve large-scale instances efficiently. A case study based on real flight schedule data from Guangzhou Baiyun International Airport demonstrates the effectiveness of the proposed method. The proposed framework identifies an optimal airside PV capacity of 559.60 kW, reducing the airport ground operator’s annual total cost by 624,042.60 CNY relative to the no-PV case and achieving a PV payback period of 3.04 years. Compared with conventional vehicle-to-pile ratio planning, the optimized strategy reduces the E-GSE fleet from 198 to 162 and decreases the number of fast chargers from 89 to 73, corresponding to reductions of approximately 18% in both categories.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687381</guid>
    </item>
    <item>
      <title>Learning from geometry-aware near misses to real-time COR: A corridor-wide grouped random parameters GEV framework</title>
      <link>https://trid.trb.org/View/2687376</link>
      <description><![CDATA[Real-time corridor-wide crash-occurrence risk (COR) prediction is challenging, since existing near-miss EVT models oversimplify collision geometry, neglect vehicle–infrastructure (V–I) interactions, and fail to adequately account for spatial heterogeneity in traffic and roadway conditions. To do so, this study develops a geometry-aware 2D-TTC near-miss extraction and integrates it with a hierarchical Bayesian structure grouped random parameters (HBSGRP–UGEV) to estimate short-term COR in urban corridors. Building on prior grouped EVT formulations while explicitly accommodating both V–V and V–I near-miss processes within a unified corridor-wide modeling framework. High-resolution trajectories from the Argoverse-2 dataset were analyzed across 28 sites on Miami’s Biscayne Boulevard to extract extreme near-miss events. The model incorporates vehicle dynamics and roadway features as covariates, with partial pooling across segments and intersections to capture corridor-wide heterogeneity. Results show that the HBSGRP–UGEV framework outperforms fixed-parameter HBSFP-UGEV models, reducing DIC by up to 7.5% (V–V) and 3.1% (V–I). Predictive validation using ROC–AUC confirms strong accuracy (0.89 for V–V segments, 0.82 for intersections, 0.79 for V–I segments, and 0.75 for intersections). Grouped random-parameters (HBSGRP) framework indicate that relative (speed, distance, and deceleration) dominate V–V near-miss risk on segments, whereas V–I segment risk is primarily associated with relative distance; at intersections, V–V risk is driven by relative (speed and distance), while V–I dynamics exhibit no statistically significant effects. These findings demonstrate the value of a geometry-aware, spatially adaptive framework for proactive corridor safety management, supporting both real-time interventions and long-term Vision Zero goals.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687376</guid>
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    <item>
      <title>Time-series risk forecasting of airport conflict hotspot with SA-GRU</title>
      <link>https://trid.trb.org/View/2663678</link>
      <description><![CDATA[Airport surface operations increasingly confront collision risks from intricate layouts, vehicle-aircraft interactions, and dense mixed traffic flows. This study develops a predictive framework for conflict hotspot identification by integrating topological, network vulnerability, and traffic complexity metrics into a composite risk evaluation system. A hybrid method combining composite weighting and improved TOPSIS first identifies latent hotspots through node-level risk assessments. Temporal risk patterns are then extracted via principal component analysis of hotspot features, with future risk trajectories predicted using a GRU network enhanced by self-attention mechanisms. Validated through Shenzhen Bao'an International Airport simulations, the proposed SA-GRU model reduces RMSE by 9.14–11.55 % against benchmark models (HA/ARIMA/SVR/LSTM/GRU). Analysis reveals significant spatiotemporal variations in hotspot risks, where daily trends show similar risk fluctuation patterns across zones but differ substantially in intensity. High-risk areas dynamically shift across operational phases, emphasizing the necessity of time-sensitive predictions. The framework enables proactive identification of critical conflict zones through predictive risk monitoring, demonstrating practical potential for optimizing airport surface management. By translating multidimensional operational data into actionable safety insights, this methodology supports intelligent decision-making for collision prevention and resource allocation in complex aviation environments, while remaining adaptable to diverse airport configurations.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663678</guid>
    </item>
    <item>
      <title>SafeMTS Report: Applying Large Language Models to Maritime Near-Miss Safety Data Analysis</title>
      <link>https://trid.trb.org/View/2685588</link>
      <description><![CDATA[This report describes the methods, models and results of the application of large language models to maritime near-miss safety data to increase analytical efficiency and accuracy among diverse industry data within the SafeMTS (Safe Maritime Transportation System) program.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685588</guid>
    </item>
    <item>
      <title>Surface Transportation Reauthorization: Passenger and Freight Rail Issues</title>
      <link>https://trid.trb.org/View/2681392</link>
      <description><![CDATA[The Infrastructure Investment and Jobs Act (IIJA; P.L. 117-58) contained, among other provisions, a five-year reauthorization of federal surface transportation (highway, public transportation, and rail) programs set to expire on September 30, 2026. Funding for Amtrak or for discretionary grant programs, rail safety, and the rail industry are all issues addressed in the IIJA or prior reauthorizations that could be considered in future rail reauthorization bills. Issues discussed in this report include: (1) The overall funding level for Amtrak is typically an issue in surface transportation reauthorization debate. Amtrak’s expenses have exceeded its revenues each year; therefore, its ability to operate and undertake infrastructure improvement projects has depended on funding from Congress. (2) Apart from funding Amtrak, the IIJA provided authorizations from the General Fund of the U.S. Treasury and advance multiyear appropriations for several discretionary competitive grant programs. Unlike some highway and transit programs, rail programs do not receive contract authority from the Highway Trust Fund and therefore rely on the annual appropriations process to make funding available for grants. Congress could choose to increase, decrease, or hold steady the funding levels established in the IIJA and continue to provide multiyear advance appropriations, or it could continue to rely on annual appropriations bills to fund rail programs. (3) Several rail safety bills were introduced in the 118th and 119th Congresses. Congress could include some elements of these past proposals in potential surface reauthorization bills; it could also choose to address rail safety separately or leave existing railroad safety laws in place. (4) Congress created the Surface Transportation Board (STB) in 1995 to be the economic regulator of interstate land and water transportation providers. STB has the authority to approve railroad mergers, acquisitions, and consolidations, including the acquisition of a company’s railroad line(s). If Congress were to reauthorize STB, Congress could transfer the approval authority for railroad mergers from STB to one or more antitrust agencies, such as the Department of Justice Antitrust Division, as recommended by a study conducted by the Department of Transportation and the National Academies at Congress’s direction.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681392</guid>
    </item>
    <item>
      <title>Near miss detection framework based on multi-observational eccentric domain in confined waters</title>
      <link>https://trid.trb.org/View/2641314</link>
      <description><![CDATA[A framework for near-miss extraction in restricted waters is introduced with a novel concept of a multi-observational eccentric elliptical ship domain based on data distribution characteristics. The Cumulative Density Function constructs the target domain from the reasonable distribution of the target vessel's surrounding data. Subsequently, a framework for near-miss extraction is established by determining geometric parameters from generated domain models and the modified Ship Domain Overlap Index. A case study in restricted waters on the eastern Malacca Strait validates the effectiveness of the proposed concepts and framework. Finally, the discussion on the domain patterns and the traffic characteristics is conducted. The defined domain parameters and the extracted near-miss scenarios demonstrate that the proposed detection framework has potential in traffic pattern analysis and maritime transportation safety promotion, according to the data-driven domain-based applications.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2641314</guid>
    </item>
    <item>
      <title>Navigational risk criteria design for MASS operations using survey data in China</title>
      <link>https://trid.trb.org/View/2644009</link>
      <description><![CDATA[Traditional static risk matrices are often insufficient for assessing the multifaceted risks associated with Maritime Autonomous Surface Ships. To address this limitation, this study proposes a data-driven, scenario-adaptive risk criteria design framework. Drawing on empirical near-miss data from MASS sea trials in China, the framework identifies risk influencing factors and quantifies them via structured expert elicitation. By utilizing statistical quartiles, dynamic risk criteria are established to facilitate the adaptive visualization of risk levels. The application of the proposed framework to three real-world scenarios (grounding, communication failure, and control conflict) reveals that human error and coupled human-machine interaction risks remain the predominant sources of vulnerability. Notably, the analysis highlights cognitive polarization and divergences in risk perception between academic and industry experts. Moreover, a comparative analysis between non-linear and linear risk formulations is conducted, validating the robustness and consistency of the identified risk patterns across different model assumptions. The results demonstrate that the proposed framework effectively captures nuanced expert judgments, thereby providing a precise and robust instrument for MASS risk governance.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644009</guid>
    </item>
    <item>
      <title>Unraveling the travel patterns of Indonesian airplane passengers in choosing modal use and shifting</title>
      <link>https://trid.trb.org/View/2673310</link>
      <description><![CDATA[This study investigates the travel behavior and modal preferences of airplane passengers accessing and exiting Soekarno-Hatta International Airport (CGK) in Jakarta, Indonesia. Using Latent Class Cluster Analysis (LCCA) on survey data from 204 respondents, the research identifies distinct passenger segments based on socio-demographic, economic, and trip characteristics. Three main groups emerge for travel to and from the airport: (1) cost-sensitive leisure travelers favoring shuttle services, (2) multimodal urban commuters who combine airport trains with other public transport options, and (3) affluent professionals who prefer private vehicles or ride-hailing services for their convenience and efficiency. The results indicate that employment status, income, and travel purpose are more strongly associated with observed mode-use patterns than age and gender, and that direct, single-mode trips are preferred over transfer-based alternatives within the surveyed sample. The identified latent classes represent context-specific behavioral patterns observed among passengers surveyed at CGK during the study period. They should not be interpreted as statistically representative segments of the broader air passenger population. The findings provide indicative insights into how different types of airport users navigate availability access and egress options in a large metropolitan airport environment. They may support exploratory discussion of the design and evaluation of integrated, sustainable airport ground access strategies in similar contexts.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673310</guid>
    </item>
    <item>
      <title>Characterizing vehicle–pedestrian interaction behavior in near misses: Insights from three different cities</title>
      <link>https://trid.trb.org/View/2673145</link>
      <description><![CDATA[Improving the safety of vulnerable road users such as pedestrians requires a good understanding of their interaction behavior and their collision avoidance mechanisms in interactions with other road users. Refining this understanding will become even more important in an automated driving environment, where properly representing road users’ evasive actions is required to develop effective collision avoidance systems, especially in mixed and less organized traffic conditions. This study models vehicle–pedestrian interactions using a multi-agent Markov game modeling framework to measure the degree of cooperation as road users interact with each other (e.g., collectively try to avoid a crash). Data from three cities with different traffic environments were used, including Boston (US), Cairo (Egypt), and Singapore. The model adopts an Inverse Reinforcement Learning framework that captures road users’ utilities from their trajectories while accounting for the equilibrium in their actions. Results demonstrate substantial variations in behavior across different cities. For example, Cairo was shown to be the most cooperative environment, whereas Singapore presented the lowest levels of cooperation. Moreover, the level of cooperation is negatively associated with speed variables, which shows that road users were expected to cooperate more when they reduced their speeds. This paper provides valuable insights into road users’ cooperation levels in different environments. This is useful for accurately modeling road users’ actions and incorporating their behaviors in advanced automated driving systems, which should properly reflect local traffic environment conditions.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673145</guid>
    </item>
    <item>
      <title>A method for clustering ship driving styles in head-on situations using collision avoidance behaviour characteristics</title>
      <link>https://trid.trb.org/View/2604738</link>
      <description><![CDATA[Currently, there are limited researches on objective demonstration and mining methods concerning the existence of ship driving styles. This paper proposes a method for clustering ship driving styles in head-on situations using collision avoidance behaviour characteristics. Firstly, the head-on situations are screened based on relative motion parameters between ships. Secondly, the improved sliding window algorithm is employed to detect the collision avoidance decision-making moment, considering the ships’ manoeuvring performance and navigation inertia. Then, collision avoidance characteristic indicators are selected, which combine the four collision avoidance requirements of ”early, large, wide, clear” proposed by the International Regulations for Preventing Collisions at Sea (COLREGs). Finally, a combination of factor analysis and the K-means++ algorithms is utilized to effectively classify and characterize ship driving styles. Empirical findings derived from Automatic Identification System (AIS) data in the Laotieshan Waterway demonstrate that ships can be categorized into four distinct driving styles: Conservative Close-Distance Avoidance (CCDA), Delayed Low-Efficacy Avoidance (DLEA), Proactive Large-Amplitude Avoidance (PLAA) and Preventive Safe-Distance Avoidance (PSDA), which account for 50%, 26%, 15%, and 9% of the total, respectively. The proposed method provides a novel research perspective and certain practical application value in comprehending the micro-behavioural traits of ships and advancing the field of Maritime Autonomous Surface Ships (MASS).]]></description>
      <pubDate>Mon, 02 Mar 2026 08:55:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604738</guid>
    </item>
    <item>
      <title>Examining airport intermodal access mode choice behaviour using interpretable machine learning</title>
      <link>https://trid.trb.org/View/2659514</link>
      <description><![CDATA[Understanding the ground access travel behaviour of airport users is essential for improving airport services. While previous studies largely focus on unimodal travel, limited attention has been paid to door-to-airport intermodal access. This study examines ground intermodal access mode choice behaviour, using Beijing Daxing International Airport as a case study. We apply Extreme Gradient Boosting (XGBoost) to model mode choice behaviour, and utilise interpretable machine learning techniques including SHapley Additive exPlanation (SHAP) values and Accumulated Local Effects (ALE) plots to capture nonlinear behavioural patterns. Findings reveal that intermodal choices are strongly shaped by traveller characteristics and access/feeder travel time thresholds. The metro-private vehicle intermodal is attractive when metro travel time is 43–62 or 75–105 min, and feeder time exceeds 7 min. The airport coach-private vehicle intermodal is appealing when the feeder time is under 16 min and the coach line-haul time exceeds 79 min. Although the effect is modest, the high-speed rail–private vehicle intermodal is facilitated by a feeder time of 14–33 min. Key policy implications include time- and threshold-specific strategies, with integrated bundles and real-time coordination of feeder and line-haul services. The study advances understanding of threshold-sensitive intermodal decisions and provides insights for developing sustainable and traveller-oriented airport ground transport services.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:10:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659514</guid>
    </item>
    <item>
      <title>The myth of quick conflict-based road safety analysis: Limits of short-term conflict data in collision risk prediction</title>
      <link>https://trid.trb.org/View/2667167</link>
      <description><![CDATA[Traditional road safety analysis is reactive and often hindered by scarce collision data. Traffic conflicts, or near-misses, offer a proactive surrogate for safety assessment, using Extreme Value Theory to extrapolate collision risk from these more frequent events. However, a critical methodological issue is the lack of guidance on prediction reliability. Many studies use short observation periods, yielding predictions with unacceptably wide credible intervals and fostering a misleading impression that such studies are quick. This research demonstrates that because severe conflicts remain rare, hundreds of days of continuous data collection are required for reliable results. This paper systematically assesses the reliability of collision predictions using conflict data with EVT models. The study utilizes a large dataset of traffic conflicts that was collected continuously via LiDAR sensors at four unsignalized intersections in Kitchener, Canada, for periods of up to one year. Using a Peak Over Threshold approach in a Bayesian framework, the analysis evaluates how collision estimates, and their 95% credible intervals converge as data collection increases from two to 365 days. The results demonstrate that while mean collision predictions can stabilize with limited data, the associated credible intervals for short collection periods are so wide that they practically offer no meaningful information. This research concludes that the common practice of using a few days of data is insufficient for reliable safety analysis. It provides an evidence-based methodology for determining the necessary data collection duration, enabling practitioners to balance resource efficiency with the need for robust and reliable proactive safety assessments.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667167</guid>
    </item>
    <item>
      <title>Surface Transportation Reauthorization: Federal Highway Programs</title>
      <link>https://trid.trb.org/View/2666823</link>
      <description><![CDATA[Congress periodically passes authorizing legislation for the federal surface transportation programs. Surface transportation authorization acts establish, continue, or discontinue federal programs and establish an upper limit on the amount of funds for such programs. Previous surface transportation authorization acts covered programs related to highways, highway safety, transit, and intercity passenger rail. This report focuses on highway programs authorized in the most recent surface transportation reauthorization legislation, the Infrastructure Investment and Jobs Act (IIJA; P.L. 117-58). This includes programs authorized in Division A, Title I, of the IIJA, as well as certain programs authorized in Division J, Title VIII, that provide funding for public roads. This report addresses three selected issues likely to be of concern to Congress during development of a future surface transportation authorization act. The first issue concerns the level of funding for the highway programs and the types of highway funding. The IIJA provided an increase in highway funding compared with the Fixing America’s Surface Transportation Act (FAST Act; P.L. 114-94) and increased the highway programs’ reliance on the Treasury’s general fund. Second, Congress may contend with the potential insolvency of the Highway Trust Fund, the main source of funding for the highway programs. The Congressional Budget Office (CBO) projects that, absent a change in policy, the Highway Trust Fund’s balance will approach zero in FY2028. Third, Congress may consider changes to the highway formula and competitive discretionary grant programs to reflect shifting priorities.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:00:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666823</guid>
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