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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>‘Food deserts’ theory applied in the field of road safety: Testing the impact of traffic analysis zone size in Cape Town</title>
      <link>https://trid.trb.org/View/2689805</link>
      <description><![CDATA[Motivated by high road fatality rates in South Africa, the authors are on a quest to improve road safety analysis methodologies, adapting the “Desert theory (Hulchanski, 2010; Vanderschuren et al., 2021; Wrigley et al., 2002).In line with the ‘Desert’ theory, a Z-value calculation is conducted. Z-values are numerical measurements used in statistics to determine a value relationship to the average of a group, measured in terms of standard deviations from the mean (Heyes, 2019). Z-values are calculated per Traffic Analysis Zone (TAZ). Vanderschuren and Newlands (2024) have proven that an adaptation of the ‘Food Desert’ theory to the field of road safety is possible. In this work, smaller TAZs (52 instead of 16) are used, as the literature indicates improved modelling accuracy (Chmielewski, 2017; Altan & Ayözen, 2018).This study revealed that the Z-value range for 52 TAZs is larger than the range for 16 areas. A significant number of large TAZs show variation within the area. More importantly, in some cases, a risk or desert area turns into a low road safety risk area. This underpins the need for small TAZs.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689805</guid>
    </item>
    <item>
      <title>Developing an adaptive zoning system for city-scale activity-based travel demand modeling using OpenStreetMap data</title>
      <link>https://trid.trb.org/View/2711042</link>
      <description><![CDATA[City-scale activity-based demand models (ABMs) offer detailed insights into travel behaviors; however, their accuracy is often limited by the coarse spatial zoning used in many zone-based demand models. Conventional Traffic Analysis Zones (TAZs) aggregate data at a level that masks neighborhood-specific variations and misallocates short and non-motorized trips. To enable more realistic urban mobility analysis, this paper introduces a hybrid zoning system that adapts spatial resolution to local activity density. Using Hasselt, Belgium, as a case study, we refine official statistical sectors into high-resolution miniZones. This is achieved by applying constrained k-means clustering to OpenStreetMap building data, followed by Voronoi tessellation. The resulting clusters are transformed into contiguous zones through dissolving Voronoi tessellation. This technique is applied to provide fine-grained detail within the city’s scope, reflecting the density of buildings where human activity is high. To keep the refinements of the zoning computationally manageable for a city-scaled regional model, a gradual reduction in detail is incorporated as one moves away from the city. This is achieved by aggregating to coarser official units in the more distant regions. The final result is a hybrid zoning system. This adaptive model approach enhances the representation of trip generation and distribution within cities, providing support for more accurate activity-based travel demand modeling.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711042</guid>
    </item>
    <item>
      <title>Sustainable Traffic Management Strategies for Congested Intersections: Multi-Criteria Assessment of Al-Sa’a Intersection and Al-Jari Street in Hit City</title>
      <link>https://trid.trb.org/View/2676054</link>
      <description><![CDATA[The study aims to evaluate sustainable traffic management strategies for congested intersections in medium-sized Iraqi cities, with a focus on Al-Sa’a Intersection and Al-Jari Street in Hit City. These nodes face severe traffic congestion, delays, and infrastructure limitations that compromise urban mobility and sustainability. A multi-criteria evaluation (MCE) framework was employed to analyze three categories of interventions—engineering, planning, and administrative—based on five weighted criteria: traffic efficiency (40%), delay reduction (25%), cost (20%), environmental impact (10%), and social acceptance (5%). The methodology combined field data collection (traffic counts, travel time, and delays), GIS-based spatial analysis, and stakeholder consultation to prioritize solutions and evaluate performance. The findings indicated that all proposed solutions improved traffic performance, but varied in scope and impact. Engineering solutions, such as street widening and grade separation, reduced congestion by up to 40%. Planning measures, including public transport enhancement and alternative routes, scored the highest (8.2/10) due to their long-term sustainability and balanced environmental impact. Administrative actions—optimized signal timing and truck regulation—offered low-cost, short-term improvements. The study demonstrates the value of integrated, GIS-supported, multi-criteria approaches in diagnosing and addressing urban traffic challenges in secondary cities. A phased implementation strategy is recommended: initiate with administrative measures, transition to planning-based interventions, and apply engineering upgrades where necessary. The framework can support future transport planning in similar urban contexts across Iraq.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676054</guid>
    </item>
    <item>
      <title>Macroscopic on-street parking inventory modeling: Exploring an open data approach</title>
      <link>https://trid.trb.org/View/2665442</link>
      <description><![CDATA[Parking plays a vital role in shaping land use and transport. Despite occupying significant portions of urban space, detailed data on parking locations and capacities are often unavailable. Recognizing the critical significance of such data for comprehensive transportation modeling and sustainable urban planning, this study presents two statistical models designed to predict the available on-street parking length in urban traffic analysis zones. The first model uses OpenStreetMap (OSM) data as its primary input, while the second is based on official parking inventory data from the city of Berlin. Both models are built using multiple linear regression, with land use and built environment characteristics as independent variables. The models are evaluated by applying them to the city of Munich. This research provides new insights into the spatial distribution of urban on-street parking and offers a practical approach for estimating parking supply to support sustainable urban development strategies.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665442</guid>
    </item>
    <item>
      <title>Exploring the built environment determinants of traffic congestion with explainable machine learning methods</title>
      <link>https://trid.trb.org/View/2685149</link>
      <description><![CDATA[Traffic congestion has become an urgent problem that needs to be addressed with the rapid urbanization occurring globally. Effective planning of land use configurations is recognized as a structural solution to mitigate urban congestion. However, existing studies may inadequately address how built environment variables directly impact urban congestion, which mostly focus on simplified travel-related indicators, city-level comparisons, and linear relationships. To bridge the gaps, this paper proposes an interpretable machine learning approach to explore the nonlinear associations between urban built environments and traffic congestion, utilizing data collected from various sources. The key findings can be outlined as follows. First, the amenity density (which represents the density of consumption-oriented destinations) emerges as the dominant built environment factor influencing traffic congestion, surpassing conventional variables such as road density and population density. Then, non-linear threshold effects critically shape congestion outcomes, as key factors, including land use diversity, road network density, and metro coverage ratio, only exhibit congestion-mitigating effects beyond a specific level. Finally, the synergistic interplay of public transit network integration, job-housing balance, and high mixed land-use within connected urban spaces is crucial for regional congestion mitigation. By incorporating these insights, traffic planners can develop more effective strategies to alleviate spatially and temporally related traffic congestion within road networks.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685149</guid>
    </item>
    <item>
      <title>Multi-ship Encounter Identification Using Community Detection of Complex Network</title>
      <link>https://trid.trb.org/View/2624149</link>
      <description><![CDATA[With the increasing maritime traffic, the effective identification of multi-ship encounter scenarios has become an urgent demand for maritime management. Traditional clustering-based methods tend to generate identification errors in complex environments. This paper proposes a community detection-based approach for recognizing multi-ship encounter scenarios. Community detection is a technique that discovers collective behavior patterns through network topology analysis. In this study, we first construct a ship encounter network model incorporating dynamic ship features such as positions and headings to characterize encounter relationships among ships. Subsequently, we employ the Louvain community detection algorithm to identify communities within the network, where each community represents a multi-ship encounter scenario. Finally, a case study using real AIS data from the Yangtze River Estuary demonstrates that the proposed method can effectively identify multi-ship encounter scenarios.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624149</guid>
    </item>
    <item>
      <title>Strategies for Sustainable Urban Freight Transportation A Demand-Based Study of Noida, India</title>
      <link>https://trid.trb.org/View/2647777</link>
      <description><![CDATA[Urban freight transportation directs towards economic resilience amid India’s rapid urban growth, yet it poses significant sustainability challenges, with freight accounting for 15-20% of the nation’s GDP with 63% of the movement occurring by road. To address these issues, the study investigates freight system of Noida, using a demand-based approach that enhances vehicle-usage efficiency across economic, environmental and social dimensions. The research assesses eight key operational efficiency parameters: trip frequency, fuel consumption, emissions, empty vehicle returns, ton–kilometres travelled, congestion delays, dwell times, and goods distribution across Traffic analysis zones defined by sector divisions and land use patterns. Moreover, to capture the diverse freight services ranging from low- value bulk to business–to-consumer deliveries, the research categorises vehicles into Goods Autos, Goods Vans, Light commercial trucks, and Multi–axle commercial trucks. The research methodology integrates secondary data: land use, road networks, vehicle registrations, demographics, with primary data collected from five survey instruments targeting key stakeholders, with GIS tools establishing spatial frameworks linking routing emissions to land-use relationships. The findings demonstrate spatial-temporal inefficiencies in freight movements and reveals key operator challenges. The study recommends a spatial temporal efficiency framework and sustainable policy strategies as time route restrictions, night time freight sharing, shared logistics platforms and green logistics practices, tailored for medium sized Indian cities. These findings offer practical, robust and evidence–based guidance aiming to improve urban freight efficiency and sustainability across similar urban contexts.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647777</guid>
    </item>
    <item>
      <title>An Empirical Investigation into the Hallucination of Mathematical Optimization in Travel Demand Model</title>
      <link>https://trid.trb.org/View/2613290</link>
      <description><![CDATA[This study examines the “hallucination of optimization” in transportation modeling, where traditional optimization methods yield seemingly valid results but fail to capture the complexities of real-world travel behavior. Focusing on the gravity model and origin-destination (OD) matrix estimation, the paper critiques the limitations of these approaches while exploring the transformative potential of big data. The gravity model, grounded in simplified assumptions about population, employment, and travel costs, often achieves high validation metrics like trip length distribution fit. However, it struggles to accurately replicate OD flows, revealing a gap between mathematical validity and practical accuracy. Similarly, OD matrix estimation relies on the quality of input data and performs poorly in scenarios with low-precision initial matrices, particularly at the traffic analysis zone (TAZ) level, despite strong overall metrics. This highlights the need to reassess conventional modeling techniques and leverage evolving data conditions to improve the accuracy of travel demand models.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613290</guid>
    </item>
    <item>
      <title>Transit Network Design Using Entry-Only Smartcard Data: A Case Study in China</title>
      <link>https://trid.trb.org/View/2613206</link>
      <description><![CDATA[This paper presents an integrated approach for designing a public transit network utilizing entry-only smartcard data. The entry-only fare system, widely adopted in China, requires passengers to tap their cards upon boarding, providing limited information on travel purposes. The research commences by estimating stop-to-stop passenger origin-destination (OD). Subsequently, Traffic Analysis Zones (TAZs) are divided using a Voronoi method, incorporating a weighted combination of distance and the density of city central heating subscribers. Based on the aggregated zone-to-zone OD matrix, an initial feasible network solution is developed through heuristic rules and iteratively refined using a stochastic beam search algorithm. The proposed network design is integrated into the city’s existing road network, as derived from OpenStreetMap, using a multi-step Dijkstra algorithm. The proposed method solely relies on easily accessible entry-only smartcard data in the absence of readily available TAZs, providing valuable insights for transit network optimization oriented to practical implementation.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613206</guid>
    </item>
    <item>
      <title>Towards more granular choice set building in location choice models: A graph-structured meta-topology as an alternative to matrices in agent-based travel demand models</title>
      <link>https://trid.trb.org/View/2625689</link>
      <description><![CDATA[Microscopic and agent-based travel demand models frequently rely on precomputed matrices describing travel times, distances, costs, or interchange counts between traffic analysis zones (TAZ). While these matrices simplify computational efforts, their reliance on spatial aggregation means that finer-grained, more behaviorally realistic insights are sacrificed. To achieve a more accurate microscopic view, one would need to divide large TAZs into smaller ones. However, this solution quickly becomes problematic, as it leads to a growth in matrix size, increasing both the memory footprint and the amount of precomputation necessary to maintain these matrices. To address these limitations, we present a method that employs a precomputed meta topology, a graph structure where each node can be annotated with additional information. As a case study, we implement a constrained Dijkstra-based graph traversal algorithm on this meta topology to generate location choice sets for agent-based models. This approach significantly reduces memory requirements while introducing additional computational overhead, as it involves constructing multiple subgraphs. Although it enhances granularity and offers a more behaviorally nuanced perspective than matrix-based methods, certain trade-offs and performance considerations must be acknowledged. In the final section, we discuss how the meta topology can be further leveraged, as well as strategies for improving the efficiency and robustness of the proposed solution.]]></description>
      <pubDate>Tue, 30 Dec 2025 10:37:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625689</guid>
    </item>
    <item>
      <title>A unified framework for modeling traffic crashes from hierarchical spatial resolutions</title>
      <link>https://trid.trb.org/View/2577140</link>
      <description><![CDATA[Independent traffic crash modeling approaches do not account for the embedded relationships related to the multi-resolution data structure, leading to mis-specified estimations. The recently developed integrated frameworks demonstrate the capability of addressing this drawback. The current study proposes an integrated framework that accommodates information from multiple spatial units and observation resolutions. Specifically, the study develops an integrated model system that allows for the influence of independent variables from disaggregate crash record, micro-facility (segment and intersection) and macro (traffic analysis zone) level simultaneously within the macro level propensity estimation. The empirical analysis considers disaggregate crash records of 1,818 segments and 4,184 intersections from 300 traffic analysis zones in the City of Orlando, Florida. These crash records contain crash-specific factors, driver and vehicle factors, roadway, road environmental and weather information of each crash record. For micro-facility and macro levels, an exhaustive set of independent variables including roadway and traffic factors, land-use and built environment attributes, and sociodemographic characteristics are considered. The proposed model system can also accommodate for hierarchical correlations among the data across observation resolutions and parameter variability across the system. The empirical analysis is augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated model system relative to the non-integrated model. A validation exercise also highlights the superiority of the proposed framework. The application of the proposed integrated framework can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:54:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577140</guid>
    </item>
    <item>
      <title>Multisource methodology for traffic analysis zone definition based on the fusion of Remote Sensing, OpenStreetMap, and Floating Car Data</title>
      <link>https://trid.trb.org/View/2567305</link>
      <description><![CDATA[The choice of an appropriate Traffic Analysis Zone (TAZ) system is a critical step of travel demand modelling that is often overlooked. Studies have approached this process with the goal of favouring the homogeneity of socio-economic and geographic characteristics of the zones, also possibly taking into account the minimisation of the number of intrazonal trips. However, beyond the application of general guidelines and individual experience to specific case studies, the definition of a formal approach is still an unsolved issue. Nevertheless, the rapid ICT development and the novel big data sources allow to enhance traditional models by exploiting additional land use and spatio-temporal mobility features. This paper proposes a multisource data-driven method to support TAZ definition by identifying, through a clustering approach, zones that are homogeneous from the point of view of activities, network characteristics, and land-cover. To this end, in the proposed approach, satellite remote sensing image segmentation, OpenStreetMap layers, and Floating Car Data (FCD) are jointly exploited to define a TAZ configuration, which can be directly used as a support for planning purposes. The procedure is experimentally validated with a case study associated with the EUR district of the city of Rome, Italy, using satellite Sentinel-2 imagery, the corresponding OpenStreetMap data, and an FCD set containing more than 1.500.000 trips.]]></description>
      <pubDate>Mon, 30 Jun 2025 17:27:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567305</guid>
    </item>
    <item>
      <title>Urban Spatial Aggregation Issues in Transportation: A New Homogeneity-Related Zone System</title>
      <link>https://trid.trb.org/View/2512188</link>
      <description><![CDATA[Delineating urban land use patterns and the close relation to transport has long been a core research topic, in which most are analyzed at aggregate level from a macroscopic perspective. This study tended to create a new homogeneity-related zone system from the basic grid cells and examining if the improved system outperforms than traditional zone systems when conducting aggregate analysis in transportation. Within ride-hailing schemes, the selection process of drop-off locations of each trip is fully recorded by the mobile transportation platform, which offers a promising way in addressing the spatial aggregation issue. Specifically, on the basis of destination retrieval record (DRR) data, this study explored the potential relevance among them and built homogeneity pairs. Based on the improved cluster algorithm, homogeneity pairs are used to aggregate basic grid cells into the homogeneous traffic analysis zones (HTAZs) that are high in inter-zone consistency. The third ring area of Chengdu City as a case study was divided into 260 HTAZs using the proposed partition approach. Newly developed homogeneity-related zone system performs better than traditional systems in terms of geometry and internal consistency. Findings from this study also suggest that the zone partition should maintain internal consistency, namely homogeneity, as much as possible so that the aggregate-level spatial units can reflect and summarize the features of intra-zone individuals. This study demonstrates the mining potential of trip record data within transport systems, and provides a more reliable spatial structure to reveal travel patterns and conduct transportation design.]]></description>
      <pubDate>Fri, 13 Jun 2025 14:56:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512188</guid>
    </item>
    <item>
      <title>Availability of Secondary Data for Determining Employment and Sales by Traffic Zones</title>
      <link>https://trid.trb.org/View/2548972</link>
      <description><![CDATA[Reliable sources of secondary information on a wide range of land uses, land use activities, and socioeconomic characteristics are needed to facilitate the development of various projections and estimates by traffic zone. To the extent that appropriate secondary data proves satisfactory; the expensive and time-consuming process of primary data collection can be reduced. This report discusses the availability of employment and retail sales data from existing data sources for the state of Texas.]]></description>
      <pubDate>Mon, 02 Jun 2025 15:21:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548972</guid>
    </item>
    <item>
      <title>The Effect of Zone Size on Traffic Assignment</title>
      <link>https://trid.trb.org/View/2548973</link>
      <description><![CDATA[The purpose of this study was to evaluate the effect of zone size on assigned link volumes. This was accomplished by comparing the volumes assigned to a common basic network using three different zone-size configurations. Assigned link volumes using medium and large zones (one half square mile and one square mile zones, respectively) were compared with those using small (quarter square mile) zones. The differences in assignments between the small-zone and the medium-zone systems were generally considerably less than those between the small-zone and the large-zone systems. The average differences in assigned link volumes relative to the small-zone system was 5.8 percent for the medium-size zone system and 13.2 percent for large-zone system for assigned link volumes in excess of 25,000 vpd. Of the links exceeding 100 vpd in the small-zone system, approximately 45 percent had assignments that differed by more than 10 percent when compared to the medium-zone system. The corresponding value was 60 percent when comparing the large-zone system with the small-zone system. Absolute differences in assigned link volumes were stratified into six volume groups. It was found that the medium-zone system consistently had assigned link volumes that were closer to that of the small-zone system than did the large-zone system for each volume group. Compared with the small-zone system, there was a decrease of interzonal trips of 3.0 and 8.4 percent when going to medium-zone and large-zone systems, respectively. It is concluded that zones as large as a half square mile can be used without serious or practical effect on the traffic assignment results. This conclusion is considered valid for medium size urban areas and for low density areas (such as single family residential) in any urban area.]]></description>
      <pubDate>Mon, 02 Jun 2025 15:21:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548973</guid>
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