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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Uncovering the differential effects of metro network expansion on commuter and non-commuter travel</title>
      <link>https://trid.trb.org/View/2698614</link>
      <description><![CDATA[The continuous expansion of metro networks profoundly reshapes the spatial–temporal patterns of commuting and non-commuting travel. Although numerous studies have extensively examined the relationship between the built environment and metro passenger ridership, few have specifically addressed the differences between commuting and non-commuting travel. This study examines the effects of metro network expansion on commuting and non-commuting passenger ridership by applying smart card data from Wuhan, China (2014–2019). We select a dynamic panel Generalized Method of Moments (GMM) model to investigate how built environment characteristics influence commuting and non-commuting passenger ridership. The findings indicate that metro network expansion significantly increases both commuting and non-commuting ridership. However, commuting travel exhibits a more pronounced growth, along with high temporal concentration and route stability. Moreover, the results reveal significant heterogeneity in the effects of the built environment. Specifically, the number of enterprises and shopping centers positively shapes commuting ridership, whereas the density of restaurants and tourist attractions exerts a stronger influence on non-commuting travel. Both commuting and non-commuting ridership exhibit significant temporal persistence, with commuting ridership showing stronger dependence on its historical levels. These findings enhance understanding of how metro network expansion shapes the spatial–temporal evolution of commuting and non-commuting travel and provide empirical insights for transit-oriented development (TOD) strategies aimed at sustainably optimizing the built environment.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:44:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698614</guid>
    </item>
    <item>
      <title>A dual-stream cross-attention transformer-based framework for detecting vessel overtaking behavior in bridge waters</title>
      <link>https://trid.trb.org/View/2753290</link>
      <description><![CDATA[To address the dynamic evolution of vessel overtaking behavior and the difficulty of identifying atypical events in bridge waters, this paper proposes a Dual-Stream Cross-Attention Network (DS-CAN). AIS data from Bridge Waters of the Wuhan Yangtze area are processed through cleaning, trajectory extraction, and time regularization to construct vessel interaction segments. Based on inland collision avoidance rules, overtaking events are automatically labeled using relative bearings and longitudinal position inversions. A continuous risk metric is established by integrating spatial proximity, motion trends, and course consistency. The DS-CAN model separately encodes a Trajectory Sequence Stream (TSS) and an Expert Prior Stream (EPS), and employs a cross-attention mechanism to fuse heterogeneous features, enhancing the representation of complex overtaking patterns. Experimental results show that DS-CAN achieves an F1-score of 0.9177 and a Recall of 0.9130, outperforming baseline models such as XGBoost and LSTM. Moreover, the model demonstrates strong robustness in the tested bridge water environments, including late-stage overtaking completion and ambiguous dynamic evolution. The proposed method effectively improves intelligent overtaking detection and provides technical support for maritime safety supervision in bridge waters.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:15:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2753290</guid>
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    <item>
      <title>Bridging the digital divide in urban mobility: a latent class choice analysis of older adults’ preferences for autonomous vehicles in China</title>
      <link>https://trid.trb.org/View/2716748</link>
      <description><![CDATA[As China experiences rapid population aging, ensuring inclusive and sustainable mobility for older adults has become a pressing policy concern. Autonomous vehicles (AVs) offer the potential to enhance mobility independence, yet their adoption among older adults remains uneven due to heterogeneous psychological dispositions and digital capability constraints, particularly in app-mediated AV systems. This study examines how digital capabilities and behavioral mechanisms jointly shape older adults’ preferences for private and shared AVs, using a hybrid modeling framework. A mixed-mode survey of 996 older adults in Wuhan, China was conducted, combining a stated preference experiment, Theory of Planned Behavior (TPB) indicators, and a five-domain digital capability scale aligned with DigComp. A sequential hybrid approach was applied: latent psychological constructs were first estimated using Structural Equation Modeling and subsequently incorporated into a Latent Class Choice Model (LCCM) to capture unobserved preference heterogeneity. The results identify two distinct segments. A public-transport-preferring group (54.82%) exhibits strong cost and time sensitivity, limited AV experience, and lower procedural digital skills, while an AV-preferring group (45.18%) shows higher baseline propensity toward AVs, greater willingness to pay for time savings, and stronger capabilities in communication and content creation. Behavioral intention and perceived behavioral control are robust predictors of AV choice, whereas subjective norms exert a consistently negative influence and trust in automation does not emerge as a stable driver once usability and capability constraints are considered. Elasticity and willingness-to-pay estimates further reveal that price-based interventions are most effective for the transit-preferring segment, whereas time and convenience improvements drive responses among AV-preferring users. These findings highlight the importance of segment-specific AV policies, emphasizing simplified access and targeted pricing for digitally constrained users, and service-quality and usability improvements for digitally proficient older adults.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716748</guid>
    </item>
    <item>
      <title>Do electric vehicles increase driving? Behavioral and spatial evidence of rebound effects from causal and spatial machine learning</title>
      <link>https://trid.trb.org/View/2762232</link>
      <description><![CDATA[While electric vehicles (EVs) are promoted as low-carbon alternatives, evidence regarding their behavioral and spatial consequences remains limited. This study investigates these impacts using causal and spatial machine learning on 2020 travel diary data from Wuhan, China. We estimate the effect of EV ownership on daily vehicle kilometers traveled (VKT) while characterizing the non-linear, spatially heterogeneous influence of the built environment. Results from the causal model indicate that EV ownership is associated with an approximately 32% increase in VKT after accounting for confounders. Furthermore, electrification partly modifies established travel–built environment interactions: several built environment variables show nonlinear associations with VKT, and the car-reducing effects of compact development and public transit accessibility appear weaker among EV-owning households. The spatial model reveals a core-periphery pattern in two respects. First, EV-related VKT growth is concentrated in metropolitan peripheries, suggesting that reduced driving costs may support longer-distance travel and reinforce outward urban expansion. Second, the built environment–VKT relationship is not only nonlinear but also spatially heterogeneous along a core-periphery gradient. In particular, current built environment conditions in metropolitan fringe areas tend to further increase predicted EV-related VKT. Consequently, achieving sustainable transportation requires demand-side management tailored to spatial variations to complement technological substitution. By integrating causal and spatial machine learning, this study develops a framework that links treatment-effect estimation with local spatial diagnosis, enabling the identification of priority areas for built environment intervention and spatially differentiated planning policies.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762232</guid>
    </item>
    <item>
      <title>Capacity of multimodal public transport systems to mitigate urban heritage island effect: Insights from Wuhan, China</title>
      <link>https://trid.trb.org/View/2762103</link>
      <description><![CDATA[Urban heritage is increasingly threatened by marginalization and isolation amid rapid urbanization, hindering its systematic conservation and adaptive reuse. Rebuilding network linkages between heritage sites is essential to overcome limitations of traditional site-based protection. While public transport is recognized as key to activating the potential of urban heritage, there is a lack of empirical evidence and model-based support regarding the role of multimodal public transport systems in reshaping heritage connectivity. This study integrates topological network analysis with an island-identification model to assess the impact of major public transport systems on the connectivity of heritage nodes in Wuhan—a city heavily reliant on public transit. Specifically, it examines the capacity of the network based on a multimodal public transport system (NMPTS) to mitigate the urban heritage island effect and validates its robustness using percolation theory. The results show that NMPTS significantly enhances heritage connectivity across multiple temporal scenarios and exhibits strong robustness, thereby playing a positive role in alleviating the heritage island effect. Only 8% of heritage nodes remain isolated. The proposed framework quantifies the capacity of multimodal systems to reduce heritage isolation and reveals variations in performance across transport modes and potential risks of centralization. These findings offer insights into promoting heritage equity and sustainability through coordinated optimization of transport systems.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762103</guid>
    </item>
    <item>
      <title>The arrival of green transportation — Are our major cities ready with charging infrastructures?</title>
      <link>https://trid.trb.org/View/2767491</link>
      <description><![CDATA[Addressing the persistent spatial mismatch between electric vehicle (EV) charging supply and dynamic urban demand, this study proposes a novel, spatially-aware optimization framework coupling the Gaussian Two-Step Floating Catchment Area (Ga2SFCA) method, Geographically Weighted XGBoost (GeoXGBoost), and the NSGA-II multi-objective evolutionary algorithm. Taking the areas of Wuhan City and Changsha City as case studies, accessibility was rigorously quantified at a uniform H3 hexagonal grid scale, integrating dynamic daytime mobility heatmaps to effectively mitigate the Modifiable Areal Unit Problem (MAUP). The GeoXGBoost-SHAP semantic diagnostics uncovered intense cross-city spatial non-stationarity in built-environment determinants. Furthermore, a multi-scenario ablation study demonstrates that conventional unconstrained siting models are structurally flawed: ablating spatial equity penalties triggers a “central clustering” infrastructural concentration in core of the urban built-up area, while ignoring machine-learning-derived suitability weights leads to “blind gap-filling” and idle ghost stations in rural fringes. Guided by a dual-weight spatial tensor balancing equity urgency and environmental suitability, the optimized NSGA-II solver generated robust Pareto fronts across constrained resource budgets (Ψ=100∼1500). By extracting short-, medium-, and long-term phased deployment strategies, the coupled engine successfully navigated unviable “spatial investment traps” (e.g., hyper-congested cores) to reallocate infrastructure toward high-return fluid corridors and pericentral extended buffers. Ultimately, this framework provides precision-guided, context-aware decision support for developing equitable and financially resilient smart transportation ecosystems.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767491</guid>
    </item>
    <item>
      <title>Source/sink-centric mobility cube analytic framework to discover multi-level urban mobility patterns</title>
      <link>https://trid.trb.org/View/2710221</link>
      <description><![CDATA[A central challenge in hub-centered mobility analytics is to bridge short-term trajectory variability with long-term structural regularity around source/sink areas (SSAs). Existing mobility mining methods can describe flow volumes or frequent routes, but provide limited insight into how localized SSA interactions generate stable flow structures and how those flow structures are reshaped by branching, merging, and disturbance. To address this gap, a source/sink-centric analytic framework is presented based on a Source/Sink-Centric Mobility Space–Time Cube (SSM-Cube), where each source/sink area defines a functional reference system that organizes hub-triggered (or hub-terminating) episodes and encodes localized interactions via operator-grounded triplets. On this representation, a progressive, generative pipeline (SSC-PGA) links micro-level states to meso-level radial/circling paths through constrained path growth, while logging branching/merging/disturbance interactions online to construct macro-level path-interaction networks. Empirical evaluations using taxi GPS data from Xiamen and Wuhan demonstrate the proposed framework can recover that the extracted SSA-centered structures are more spatially extensive and temporally persistent than baseline models (e.g., TRACLUS, AOFC, and CTM). On a representative SSA, the framework achieves the largest spatial coverage (0.157), complete temporal coverage (1.000), and a larger radial propagation radius (5.28 km), while still covering 37.5% of trips. External validation further shows that disturbance hotspots align with POI-dense and road-dense areas and exhibit lower mean speed, higher speed variability, and higher stop-and-go ratio, supporting hub management and resilience-oriented mobility diagnostics.]]></description>
      <pubDate>Thu, 27 Aug 2026 10:04:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2710221</guid>
    </item>
    <item>
      <title>An anomalous behavior detection framework for inland vessels using automatic identification system data</title>
      <link>https://trid.trb.org/View/2706421</link>
      <description><![CDATA[To address the challenges of inadequate parameter optimization and the latency caused by reliance on manual rules, this study proposed a BOA-XGBoost framework integrating trajectory clustering and supervised learning. The Butterfly Optimization Algorithm (BOA) is introduced to adaptively optimize key parameters for both DBSCAN clustering and the XGBoost classifier. In addition, an adaptive and hybrid DP-based vessel trajectory compression strategy is designed to reduce noise while preserving navigational semantic features. To detect anomalous behaviors in inland waterways, a vessel behavior representation system is constructed by jointly characterizing trajectory morphology and multidimensional lateral motion features. Comparative experiments conducted in the Hanjiang River Confluence in the Wuhan reach of the Yangtze River (HRC-Wuhan) and the WISCO Transverse Crossing Area (WTCA) demonstrate that the proposed BOA-XGBoost model achieves detection accuracies of 98.06% and 93.40% for two representative anomalous behaviors—illegal downstream navigation and early river-crossing, respectively—outperforming all baseline models across multiple performance metrics. Furthermore, ten-fold cross-validation and SHAP-based interpretability analysis confirm the robustness and interpretability of the proposed model. The proposed framework effectively identifies anomalous vessel behaviors in complex inland waters, providing reliable technical support for intelligent inland waterway traffic supervision.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706421</guid>
    </item>
    <item>
      <title>Leaving home for health care: An examination of the determinants of intercity patient mobility for obstetric care in Hubei Province, China using spatial econometric models</title>
      <link>https://trid.trb.org/View/2714535</link>
      <description><![CDATA[The uneven distribution of high-quality medical resources and intercity patient mobility have attracted increasing attention. However, evidence from developing contexts such as China remains limited, and the multidimensional determinants of these patterns are not yet well understood. On the basis of obstetric inpatient records in Hubei Province, a push–pull–barrier framework and spatial econometric models are used to examine the determinants of intercity patient mobility for obstetric care at tertiary hospitals. The results reveal that 6.30% of obstetric patients seek care across prefecture boundaries. As the provincial center that attracts patients from elsewhere, Wuhan plays a pronounced role, and the areas surrounding Wuhan experience high ratios of intercity patient mobility. Spatial econometric analyses demonstrate that insufficient local medical resources, long travel distances to local hospitals, and proximity to regional medical centers significantly promote intercity patient mobility, whereas limited insurance support and poor transportation accessibility act as institutional and geographical barriers. These findings provide policy insights for the governance of cross-regional patient mobility and the regional planning of medical resources, particularly in the context where medical resources are highly concentrated and medical insurance is administratively fragmented.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714535</guid>
    </item>
    <item>
      <title>Spatiotemporal network effects and mechanisms of charging station utilization patterns</title>
      <link>https://trid.trb.org/View/2701641</link>
      <description><![CDATA[The widespread adoption of electric vehicles (EVs) is essential for mitigating climate change. However, deployment of charging stations (CSs) has not kept pace with rapid EV growth, largely because of insufficient strategic planning. This study investigates CSs in Wuhan, China, using long-term, high-resolution charging records to generate typical 15-minute utilization rate curves. First, we identify four distinct utilization patterns using unsupervised clustering and validate their robustness through a static classification model. This step confirms that the identified temporal patterns are not random artifacts but are intrinsic to specific built environments and long-term operational attributes. Subsequently, by constructing a novel dynamic spatial network using real-time traffic data, this study systematically quantifies the competition, agglomeration, and spillover effects within the CS network. Results show that station usage is embedded in a complex spatial network, revealing distinctive spatiotemporal mechanisms across the identified patterns. Based on these findings, we propose pattern-specific spatial optimization strategies. Following a “pattern identification—pattern validation—mechanism analysis—spatial optimization” framework, this study provides both theoretical insights and practical guidance for CS planning.]]></description>
      <pubDate>Thu, 06 Aug 2026 09:22:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701641</guid>
    </item>
    <item>
      <title>Coupled Analysis and Simulation of Safety Risks in Deep Foundation Pit Construction for Subway Stations Based on the N-K Model and System Dynamics</title>
      <link>https://trid.trb.org/View/2696901</link>
      <description><![CDATA[Risk-coupling is a key causative factor triggering accidents in the construction of deep foundation pits for subway stations, representing the interactive relationships among various risk factors within complex systems. However, existing risk management methods struggle to effectively quantify their static coupling effects and dynamic evolution mechanisms, making it difficult to accurately identify which coupling scenarios are significant and challenging to proactively prevent and control safety risks. To address this issue, this study proposes a risk-coupling measurement model integrating the N-K model and system dynamics, analyzing the coupling mechanisms among risk factors from static and dynamic dimensions, and conducting simulation verification using a Wuhan Metro deep foundation pit project as a case study. By adjusting coupling coefficients and simulation duration, the simulation results indicate that: (1) heterogeneous personnel-operation risk-coupling has a particularly significant impact on the system’s safety level; and (2) personnel violation of operational procedures and instability of support structure system are key risk-coupling factors. Based on the simulation analysis results, targeted dual strategies of source control and pathway blocking are proposed to effectively mitigate risk-coupling effects. The hybrid model constructed in this study overcomes the limitations of traditional static or single dynamic assessments, providing a transferable risk-coupling analysis paradigm for systematically quantifying the static interactions and dynamic evolution of risk factors in complex engineering projects. This not only expands the theoretical boundaries of construction safety risk management research but also offers decision-making support for proactive risk control in various complex engineering projects worldwide.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696901</guid>
    </item>
    <item>
      <title>Carbon Emissions Calculation Model and Energy Consumption Sensitivity Analysis for the Materialization Stage of Large-Span Stayed-Cable Bridges</title>
      <link>https://trid.trb.org/View/2736489</link>
      <description><![CDATA[To support the achievement of dual-carbon goals in cable-stayed bridge construction, this study establishes quantitative carbon-emission models for each materialization phase using the carbon-emission factor method. A collinearity-corrected carbon accounting approach is then developed by integrating LASSO regression to quantify emissions during the materialization stage systematically. The proposed methodology is validated through a case study of bridge-culvert engineering in a cable-stayed bridge project. The materialization-phase emissions are categorized by production, transportation, and mixing processes of raw materials. Applied to the Dongkou Yangtze River Bridge in Wuhan’s Fourth Ring Road, the model analyzes: 1) material consumption (steel: 31.75×104t, cement: 30.18×104t); 2) on-site/off-site machinery operation (excavators: 95,238t CO2-eq); and 3) emission intensity by construction division. Key findings include total materialization-phase emissions: 915,833t (direct:87,183t, indirect: 828,649t). Bridge-culvert works dominate emissions (direct: 83.4%, indirect: 95.5%). Sensitivity analysis identifies low-carbon steel (β=0.38), cement (β=0.18), and diesel (β=0.21) as critical mitigation levers. This study demonstrates that the data quality of key materials (steel, cement) and energy (diesel) significantly affect accounting accuracy (±2.8% uncertainty).]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736489</guid>
    </item>
    <item>
      <title>Evaluating the Vulnerability and Impact Zones of Urban River-Crossing Corridors: A Case Study of Wuhan, China</title>
      <link>https://trid.trb.org/View/2685639</link>
      <description><![CDATA[Urban river-crossing corridors, such as bridges and tunnels, are critical yet vulnerable links in metropolitan transportation networks, where disruptions can cause severe system-wide impacts due to limited redundancy and concentrated traffic flows. This study proposes a novel dual-indicator framework integrating vehicle detour efficiency and public transportation substitution capacity to comprehensively evaluate the vulnerability and substitutability of urban river-crossing corridors. The methodology is empirically applied to Wuhan, China—a megacity characterized by a dense network of river-crossing infrastructure. Using extensive ride-hailing trajectory data, multitemporal travel simulations, and spatial analysis of traffic analysis zones (TAZs), we systematically assess the vulnerability patterns and substitution dynamics of nine key Yangtze River crossings. Our findings reveal significant heterogeneity in vulnerability levels: peripheral corridors exhibit markedly higher vulnerability and wider impact zones than central corridors due to lower bridge density and insufficient public transit alternatives. The study also identifies spatially contiguous strip-shaped vulnerability impact areas and highlights that residents in peripheral zones face greater challenges in shifting from private vehicle to public transit during corridor disruptions. These insights provide critical implications for enhancing the resilience of river-crossing networks while also informing strategic urban transportation planning in riverine cities.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685639</guid>
    </item>
    <item>
      <title>Design for all: Land use and street design interventions to promote all-age active travel</title>
      <link>https://trid.trb.org/View/2679114</link>
      <description><![CDATA[Promoting active travel (AT) across all age groups is essential for reducing chronic disease risks in rapidly urbanizing environments. However, most current built environment (BE) interventions and related research fail to account for the nonlinear and age-specific effects of BE on AT, often assuming uniform impacts regardless of age. Addressing this gap, the present study applies machine learning (XGBoost) and SHAP interpretability methods to explore how land use and streetscape attributes influence AT behaviors among children (0–17), young adults (18–59), and older adults (60+) in Wuhan, China. The findings reveal pronounced age differences: the BE exerts a stronger influence on AT for children and older adults compared to young adults, and most BE variables display distinct nonlinear associations with AT outcomes. By quantifying these association thresholds and systematically comparing them across age groups, this study provides robust empirical evidence for the development of more precisely targeted, age-responsive BE interventions, advancing the goal of “design for all” in urban planning and public health.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:54:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679114</guid>
    </item>
    <item>
      <title>Does residing in central urban areas increase private vehicle use? A double machine learning method to address residential self-selection bias</title>
      <link>https://trid.trb.org/View/2712753</link>
      <description><![CDATA[There is ongoing debate about whether residing in central urban areas reduce private vehicle use, given contrasting findings reported across various urban contexts. These inconsistencies are partly due to residential self-selection bias, which often distorts the observed association between built environment and travel behavior. Furthermore, the nonlinear relationship among residential location, travel attitude, and travel behavior is often overlooked. To address these gaps, we assess the actual effect of central-urban residence on private vehicle use while accounting for residential self-selection biases using travel survey data in 2020 from Wuhan, China. Furthermore, a double machine learning approach is employed to model nonlinear interactions among residential location, travel behavior, and travel attitudes. Results show that central-urban residence increases weekly driving distance by 4.5 km per household (p < 0.05), accounting for 32.4% of variation in vehicle use after adjusting for residential self-selection. This suggests that residing in central urban areas may increase private vehicle use in a high-density city in China. These findings underscore the need for targeted policies to promote sustainable transport in urban China.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:55:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712753</guid>
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