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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>Route repetition and activity spaces: spatial networks, routes, stops and routines</title>
      <link>https://trid.trb.org/View/2679491</link>
      <description><![CDATA[Activity spaces are a longstanding geographic concept used to describe the mobility patterns of an individual. The proliferation of disaggregate space-time data now enables a sophisticated network-orientated approach to measuring and visualising these spaces. The current study leverages individual Global Positioning System trajectories from 365 participants over a seven-day period to introduce ‘route repetition’, a novel network-based metric quantifying habitual path selection within street networks. Using piecewise structural equation modelling with spatial controls, we demonstrate two key findings: higher route repetition is linked to lower crime rates in neighbourhoods (β = −0.24, p < 0.01); higher route repetition is also associated with longer movement duration (i.e., people who repeat routes more tend to spend more time traveling for a trip) (β = 0.24, p < 0.01). The network-centric conceptualisation of activity spaces advances beyond traditional elliptical and kernel density approaches by capturing the topological constraints of urban infrastructure. Our route repetition measure offers methodological innovation allied with substantive insight into routine spatial behaviour, providing a nuanced framework for analysing neighbourhood mobility patterns and their complex relationships with urban environmental factors such as crime exposure, land use diversity, socio-demographic composition and street network configuration.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679491</guid>
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    <item>
      <title>Evaluation of Surveying Workforce Needs to Support Highway Construction</title>
      <link>https://trid.trb.org/View/2712197</link>
      <description><![CDATA[The U.S. surveying and geomatics profession is a multidisciplinary field requiring extensive knowledge of math, science, and geography. Civil engineering curricula may provide only limited exposure to surveying and geographic information systems (GIS), which may contribute to the growing shortage of the technical talent needed for delivering accurate and timely survey data. This challenge is further compounded across all education levels with limited enrollment in existing geomatics programs and limited availability of geomatics and geospatial technology courses, even as the use of geospatial information through digital devices continues to increase.

In addition to the intellectual and technical considerations associated with effectively utilizing geospatial data, several factors can influence its broader adoption and application. One key consideration is the learning investment needed to fully leverage geospatial datasets while adapting to this evolving field to maintain proficiency. Furthermore, recent studies have highlighted growing workforce demand in geomatics engineering and related disciplines nationwide. Therefore, research is needed to identify and assess the current surveying workforce status and needs for supporting highway construction.

The objective of this research is to help state departments of transportation (DOTs) to: (1)       Identify the educational and knowledge gaps contributing to workforce shortages, (2)      Develop data-driven fundamentals for workforce planning, program development, and recruitment investments, and (3) Develop strategies to prepare for emerging technologies and future needs in highway construction and digital project delivery.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:38:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712197</guid>
    </item>
    <item>
      <title>Message Passing Period-Aware Imputation Network for Spatial-Temporal Traffic Missing Data</title>
      <link>https://trid.trb.org/View/2617736</link>
      <description><![CDATA[Intelligent Transportation System (ITS) is a critical component of smart cities, however, certain issues significantly limit the construction of ITS. On the one hand, as the core resource of ITS, traffic data often suffers from missing values due to sensor failure, communication interruption, and so on. On the other hand, traffic flow change is a complex dynamic process that resulted from periodic changes caused by social activities, which increases the difficulty of data completion. To address these issues, the Message Passing Period-Aware Imputation Network (MPPAIN) is proposed. Firstly, the spatial-temporal information is transmitted sequentially in time order by the Message Passing Block based on gated recurrent unit, and the missing values are preliminarily estimated. Then, the output is fed to the Period-Aware Block to find the main frequency components that represent the changes of the traffic flow in the frequency domain through the Fourier transform. Subsequently, the traffic flow is divided according to the major periods, and the second time estimation is completed by extracting the intra-periodic and inter-periodic features simultaneously through the convolutional neural network. Finally, a bi-directional structure is designed by reversing the input traffic flow in time order to further extract the spatial-temporal and periodic features from the future to the past. Experiments demonstrate that the proposed model has excellent data imputation capabilities in various simulated missing rates and missing scenarios on several real traffic datasets.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617736</guid>
    </item>
    <item>
      <title>A Spatial-Aware Temporal Modeling Network for Imitation Learning-Based Drone Navigation</title>
      <link>https://trid.trb.org/View/2617733</link>
      <description><![CDATA[Imitation learning-based drone autonomous navigation has attracted significant attention due to the ability of leveraging deep neural networks to learn the control policy from human pilot demonstrations. However, most current studies generate the control command using only a single image, overlooking the semantic information embedded in the sequential input images. While some reinforcement learning-based methods have explored the temporal modeling of sequential input images, they often overlook the spatial relations between frames and vectorize 2D information of each image into a 1D feature. In this paper, we propose a novel imitation learning-based method, termed the spatial-aware temporal modeling network (SATMN), for autonomous drone navigation using sequential images as input. Specifically, we introduce a spatial-temporal-separated modeling mechanism to extract low-resolution spatial features from original images and then perceive spatial-temporal relations among these 2D features. SATMN preserves the spatial information of each 2D image feature during temporal modeling and enables real-time onboard computing on a drone. To validate the effectiveness of the proposed method, we design a compact quadrotor platform capable of autonomous navigation using SATMN, entirely powered by onboard computing devices. Comprehensive and reproducible experiments on public datasets demonstrate the superior performance of our method compared to existing approaches.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617733</guid>
    </item>
    <item>
      <title>A Physics-Regularized Multiscale Attention Network for Spatiotemporal Traffic Data Imputation</title>
      <link>https://trid.trb.org/View/2658823</link>
      <description><![CDATA[Spatiotemporal traffic data imputation is a fundamental task in numerous smart mobility applications. Existing studies indicate that accurately estimating missing values from observed data relies on capturing the spatiotemporal dependencies in traffic data. However, such dependencies may exhibit distinct characteristics across varying spatiotemporal areas, such as local short-term traffic fluctuations versus global long-range periodic commuting patterns. The comprehensive multiscale nature of dependencies in traffic data, encompassing more than just local and global levels, has not been well explored in the literature. To address this issue, we propose a physics-regularized multiscale attention network (PRMAN) that hierarchically extracts spatiotemporal features from local dynamics to global trends. Specifically, the proposed PRMAN builds upon the novel Swin Transformer and introduces a hierarchical architecture that performs self-attention in local spatiotemporal windows. By systematically expanding the window size across layers, this hierarchical design explicitly addresses the distinct characteristics between local and global spatiotemporal dependencies at different scales. Meanwhile, a physics-regularized loss function is developed to align learned spatiotemporal dependencies with traffic dynamics described by the fundamental diagram. This improves the model’s generalizability beyond the training data, ensuring robust performance on unseen datasets. Numerical experiments on multiple benchmark datasets demonstrate that our proposed PRMAN achieves state-of-the-art performance in handling diverse and complex missing data patterns. The code and model are publicly available at https://github.com/2222ad/PRMAN]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658823</guid>
    </item>
    <item>
      <title>Attention-Based Spatial-Temporal Graph Neural Network With Long-Term Dependencies for Traffic Speed Prediction</title>
      <link>https://trid.trb.org/View/2658772</link>
      <description><![CDATA[Urbanization, characterized by the continuous growth of population and density in urban areas, has led to the expansion and increased complexity of transportation networks, exacerbating traffic congestion. Accurate traffic speed prediction is crucial for effective traffic network management and planning. As the complexity of real-world road networks increases, integrating spatial and temporal information for accurate traffic speed prediction has become a challenging research task. This paper proposes a novel approach by introducing a spatial-temporal graph neural network (STGNN)-based model to enhance the accuracy of traffic speed prediction. By employing an attention-based STGNN, we effectively capture the complex relationships among road segments in real-world scenarios. We utilize the Huber loss as the training objective to improve prediction accuracy. Furthermore, we present an architecture that incorporates Root Mean Square Layer Normalization into the Transformer and integrates the Spatial-Temporal Attention Wavenet (STAWnet) into the model backbone, enabling faster training while maintaining model stability. We evaluate the proposed model using five real-world traffic speed benchmark datasets. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art traffic speed prediction approaches.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658772</guid>
    </item>
    <item>
      <title>Geospatial identification and ranking of speed-related road traffic crash blackspots on Lagos-Ibadan expressway, Nigeria</title>
      <link>https://trid.trb.org/View/2700579</link>
      <description><![CDATA[Speed-related Road Traffic Crashes (RTCs) are a major contributor to fatalities on high-volume intercity corridors in Nigeria. This study identifies and ranks speed-related crash blackspots along the 127-km Lagos–Ibadan Expressway using Geographic Information System (GIS)–based spatial analysis. Ten years (2013−2022) of crash records obtained from the Federal Road Safety Corps Road Traffic Crash Information System (RTCIS) were analyzed using Kernel Density Estimation (KDE) and corridor segmentation techniques. The analysis identified thirty-three locations with high crash concentrations, of which eleven road sections were classified as blackspots based on a threshold of at least 95 speed-related casualties within a 550-m segment. These sections account for over 73% of recorded speed-related crashes while representing only about 4% of the corridor length. The results highlight the effectiveness of spatial crash analysis for identifying high-risk segments and provide evidence for targeted speed management, enforcement, and infrastructure improvements on Nigerian intercity corridors.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700579</guid>
    </item>
    <item>
      <title>Digital Twin of the Philadelphia’s Roosevelt Boulevard: Microsimulation Based on Real-Life Traffic</title>
      <link>https://trid.trb.org/View/2705425</link>
      <description><![CDATA[The use of digital mapping with precise GPS coordinates has allowed intelligent navigation, which is now ubiquitous in vehicles. The flat two-dimensional imagery provided by Google Maps was recently enhanced by the introduction of dynamic three-dimensional (3D) representations of the Earth. Unity3D now offer application programming interfaces that can be leveraged for geospatial applications. This technology, which offers visually compelling results for flight simulation and drone applications, opens up new opportunities for driving simulation. This paper presents an innovative approach for developing a drivable digital twin of Philadelphia’s Roosevelt Boulevard, PA, to enhance urban planning and traffic management using advanced simulation technologies. We introduce a complete pipeline that integrates geospatial imagery with data from Google Maps and OpenStreetMap through tools like CityEngine and Matlab RoadRunner, enabling the creation of highly detailed, editable 3D urban scenes. This methodology facilitates rapid modifications to urban landscapes, exemplified by the integration of a bus lane into existing road infrastructure, demonstrating significant advancements over traditional methods. The core of our approach is a dynamic traffic flow model developed within the Unity driving simulator, utilizing probabilistic distributions and real-world data from the Next Generation Simulation dataset to mirror actual traffic conditions accurately. This model supports the simulation of realistic, varied, and dynamic traffic patterns, crucial for testing and evaluating urban traffic scenarios and infrastructure changes. The implementation showcases the potential of digital twins for transforming urban planning and traffic systems by providing a reliable platform for scenario testing and decision making.]]></description>
      <pubDate>Tue, 26 May 2026 16:59:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705425</guid>
    </item>
    <item>
      <title>A micromobility solution for household logistics? Determinants of shared bicycle trailers usage</title>
      <link>https://trid.trb.org/View/2668562</link>
      <description><![CDATA[Cycling is a solution to decarbonize urban transport of people and goods. Shared solutions, such as shared bike trailers, facilitate the expansion of micromobility offerings within urban areas. However, such solutions are currently understudied, hence underestimated for freight transport in cities. This paper explains how shared bike trailer networks can have success in different urban environments, and analyses how this type of shared micromobility vehicles can be a cycle logistics solution applied to household logistics. Based on operational data of two shared bike trailer services, we have developed a model based on a Quasi-Poisson model and a clustering method with socio-demographic and geographic data that explains the use of these services. The results show success and failures factors of trailers in two different cities on different continents. Socioeconomic factors and zoning characteristics, and accessibility to transport options play significant roles in determining the success of bike trailer loans, with residential and commercial zoning being particularly influential in Montreal, while service zoning negatively affects trailer use in Brussels. Successful trailers are mainly located in residential and mixed-use areas. Trailers are used for different purposes, such as house moving, grocery shopping and leisure activities involving freight transportation. This mean a possibility for bike trailer to be one solution in the choices of alternative mobility to avoid vans or cars.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668562</guid>
    </item>
    <item>
      <title>Geospatial Analysis for Identifying Blocked Roads after Earthquakes: A Case Study from the Kahramanmaras Earthquake</title>
      <link>https://trid.trb.org/View/2692295</link>
      <description><![CDATA[Rapid identification of road blockages is essential for effective emergency response in the aftermath of large-scale urban disasters. This study introduces a geospatial methodology for detecting roads obstructed by earthquake-induced building collapses, utilizing high-resolution unmanned aerial vehicles (UAV) imagery collected after the February 6, 2023, Kahramanmaras Earthquake. A deep learning-based object detection model (YOLOv9-C) was employed to detect collapsed buildings, and subsequent intersection analysis with road network data enabled automated blocked road identification. The analysis was based on a geospatial data set comprising 4,188 UAV images systematically captured across Nurdagi, Kirikhan, Iskenderun, and Onikisubat, representing some of the most heavily impacted regions. To evaluate the framework’s robustness, two complementary strategies were adopted. The first was randomly split analysis (RSA), which randomly partitioned the data set into training, validation, and testing subsets. The second was Nurdagi test analysis (NTA), which excluded all Nurdagi data during training and validation and used it exclusively for testing to assess generalization in unseen areas. The blocked road detection yielded precision values of 0.963 (RSA) and 0.961 (NTA), closely aligning with a manually labeled ground truth data set. Overall, 21.55 km of blocked roads were detected across the study area. By integrating UAV-based deep learning detection with GIS, the proposed framework provides near real-time, scalable insights to support search-and-rescue operations and post-disaster logistics.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692295</guid>
    </item>
    <item>
      <title>Deconstructing Citywalk: A Multilayered Network Analysis of Urban Leisure Walking in Shanghai, China, from a Social Media Perspective</title>
      <link>https://trid.trb.org/View/2663093</link>
      <description><![CDATA[This study introduces a novel “multi-dimensional network spatial organization” framework to decode the emerging Citywalk phenomenon in Shanghai, China. By constructing and coupling three layers of networks—functional association, spatial proximity, and path support—based on 23,436 geotagged social media posts and 6,097 point of interest records, this research offers a new lens to examine the complex spatial dynamics of urban leisure walking. The functional network analysis reveals Citywalk’s polycentric, heterogeneous spatial structure, with a significantly weakened center-periphery pattern (power-law exponent = ‒1.34, 𝘗 < 0.01). The spatial proximity network uncovers a coexistence of individual spontaneity (density = 0.0037) and group organization (clustering coefficient = 0.4067, 𝘗 < 0.01), while the path support network highlights the micromechanisms of walking behavior reshaping urban space (modularity 𝘘 = 0.7425, 𝘗 < 0.01). The multilayer network coupling analysis indicates a fragmentation within contraction pattern, with community numbers increasing from 33 to 128 (𝘗 < 0.01) after incorporating behavioral dimensions, suggesting that homogeneous spatial zones are being replaced by heterogeneous behavioral domains. These findings extend the complex network theory to explain urban spatial organization and expand the boundaries of behavioral geography. The multidimensional network spatial organization framework provides a replicable analytical approach for studying similar social media–driven spontaneous walking activities. The research yields three practical planning tools: a network community–based delineation method for natural renewal units, a hierarchical optimization strategy for walking systems based on path support intensity, and a precision intervention mechanism based on functional clusters-linkage corridors. This integration of network science with urban planning provides a systematic solution to enhance urban walkability applicable to postindustrial cities globally, contributing to both theoretical advancement and practical innovation in urban studies.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663093</guid>
    </item>
    <item>
      <title>Detection and Susceptibility Mapping of Landslide Using Multi-Temporal Lidar Digital Elevation Models: Case Study of Prince George’s County, Maryland</title>
      <link>https://trid.trb.org/View/2697858</link>
      <description><![CDATA[Landslides are among the most destructive natural hazards, particularly in regions with varied terrain, geomorphologic, and climatic characteristics. This study is focused on Prince George’s County, Maryland, one of the most landslide-prone areas in the state, with the highest number of recorded landslides in Maryland’s inventory. A remote sensing-based approach using high-resolution lidar digital elevation models (DEMs) is presented for detecting and mapping landslide-prone areas. Datasets were acquired for the years 2014, 2018, and 2020, to help study temporal changes in elevation. The methodology integrates DEM preprocessing, geomorphometric analysis, and spatial overlay within a geographic information system (GIS) environment. Key terrain factors, including slope, aspect, curvature, and DEM of difference (DoD) were derived to characterize topographic instability. Raster-based classification was used in ArcGIS Pro to identify historical and potential landslide zones. Buffer and overlay operations along critical highway corridors were included. Detected zones were categorized as stable, upward, or downward displacements caused by various factors and based on the relative vertical movement of the land surface. Validation was conducted through comparison with historical landslide inventories, overlaid on all causative geomorphometric factors, as well as using reclassification methods proposed in previous work. The resulting landslide susceptibility map provides a detailed spatial representation of terrain deformation across the study area over the years, offering valuable insights for land-use planning, infrastructure development, and hazard mitigation. This research demonstrates the effectiveness of lidar-integrated geospatial analysis for regional-scale landslide detection and risk mapping.]]></description>
      <pubDate>Sat, 02 May 2026 15:47:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697858</guid>
    </item>
    <item>
      <title>Do boulevards play a role as pedestrian corridors? Unpacking an urban paradigm with pedestrian count data</title>
      <link>https://trid.trb.org/View/2673214</link>
      <description><![CDATA[Cities increasingly prioritize pedestrian-friendly environments to enhance sustainability and accessibility. Boulevards are often considered iconic pedestrian-friendly streets, balancing vehicular traffic with active transport. However, despite their reputation as key pedestrian corridors, there is limited scientific evidence supporting this notion with actual pedestrian count (PC) data. More broadly, big data technologies remain underutilized in pedestrian monitoring compared to motorized traffic research. This study utilizes longitudinal data from a mobile application in Tel-Aviv, Israel, to examine PC in boulevards compared to other streets across varying temporal contexts. The analysis spans multiple scales- from a city-wide perspective to neighbourhoods and detailed street sections- incorporating geospatial characteristics, such as network centrality, bus stations, trees, lighting, and proximity to destinations. City-scale findings show that boulevards attract more pedestrians on weekdays, with no statistically significant differences observed on weekends in most seasons. Furthermore, the role of boulevards within their neighbourhood varies over time - some function as key pedestrian routes during specific times, e.g., certain seasons or weekdays vs. weekends, while the majority showed no notable difference compared to nearby streets. A detailed examination of PC in selected boulevards and their parallel streets suggests that while boulevard-specific design features—such as medians or enhanced landscaping—may improve walking experience, it does not necessarily translate into increased walking volumes. Streets with basic pedestrian-supportive features can be just as attractive for walking, even without the formal design of a boulevard.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673214</guid>
    </item>
    <item>
      <title>A Dynamic 3D Multi-Object Tracking Method Based on Spatiotemporal Features</title>
      <link>https://trid.trb.org/View/2659134</link>
      <description><![CDATA[3D multi-object tracking is one of the important research directions in computer vision and holds significant research value in the field of autonomous driving. Relying solely on single image information or point cloud information is insufficient to overcome tracking challenges in complex scenarios. Currently, multimodal fusion 3D tracking methods still face numerous issues in fusion performance, data association, and trajectory management. Therefore, this paper proposes a dynamic 3D multi-object tracking method based on spatiotemporal features. First, a multi-scale spatial feature embedding fusion network is designed to enhance the weight of critical information within different modal features, thereby improving the prominence of target features. Second, a temporal aggregation embedding module is proposed to address the characteristics of point cloud features and fusion features, enhancing feature alignment when target features are integrated into temporal features, resulting in more robust temporal features. Finally, a multi-stage hybrid affinity dynamic association module and an adaptive dynamic trajectory management module are combined to reduce the impact of similar targets on tracking, which improves the model's ability to perceive target positions in dense scenes, and enhances the robustness of target association matching. Experimental results on the KITTI dataset have demonstrated that the proposed method achieves better tracking performance compared to other state-of-the-art methods.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659134</guid>
    </item>
    <item>
      <title>Integration of spatial database systems and sampling-based path planning for optimizing maritime navigation</title>
      <link>https://trid.trb.org/View/2587371</link>
      <description><![CDATA[This paper addresses the challenge of representing configuration spaces for sampling-based path planning in maritime navigation scenarios by using data-efficient vector maps stored in spatial database systems. The proposed approach optimizes the performance of fundamental algorithm operations in large environments, such as collision checking, by using spatial indexing to efficiently reduce the number of geometries that need to be evaluated by computationally expensive spatial predicates. An implementation combining the SpatiaLite database system, standardized electronic navigational charts (ENC) map features, and the widely used Open Motion Planning Library (OMPL) demonstrates practical applicability. The implemented system provides collision-free paths for maritime navigation, includes a graphical user interface, and is incorporated to a system for autonomous surface vehicles. Simulations show that the implementation supports multiple planning algorithms in generating valid paths in four representative large-scale maritime environments: a cluttered archipelago, a river inlet, a peninsula, and a fjord transit.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587371</guid>
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