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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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      <title>AI-assisted vehicle detection and traffic flow analysis from CCTV imagery using YOLO in the Bangkok metropolitan area</title>
      <link>https://trid.trb.org/View/2712683</link>
      <description><![CDATA[Rapid urban expansion and rising vehicle numbers in metropolitan areas have intensified traffic congestion and increased the risk of road traffic accidents. However, traditional traffic data collection methods remain limited in spatial coverage and temporal resolution, reducing their effectiveness for traffic management and road safety planning. Closed-circuit television (CCTV) systems continuously capture traffic conditions, yet their analytical potential has not been fully utilized. Advances in deep learning, particularly the You Only Look Once (YOLO) algorithm, provide strong capabilities for real-time object detection and classification from image data. This study aimed to evaluate the performance of YOLOv5m, YOLOv8m, and YOLOv12m in detecting and classifying vehicle types from CCTV video data collected from three major bridges in Bangkok, and to analyze traffic volume by time period and travel direction. The results showed that YOLOv12m demonstrated competitive overall detection performance compared with YOLOv5m and YOLOv8m. Temporal analysis revealed higher inbound traffic in the morning and greater outbound traffic in the evening, reflecting commuting patterns. In addition, the model supported traffic volume analysis across selected bridge corridors. These findings highlight the potential of integrating CCTV and YOLO for automated traffic monitoring, traffic volume analysis, road safety support, and smart transportation planning in Bangkok.]]></description>
      <pubDate>Fri, 26 Jun 2026 13:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712683</guid>
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    <item>
      <title>Understanding Carsharing User Behavior: A Spatiotemporal Approach to Stop Activity Classification</title>
      <link>https://trid.trb.org/View/2706458</link>
      <description><![CDATA[Understanding how travelers use carsharing services is critical for optimizing operations and guiding urban mobility policy. Traditional data collection methods, such as surveys and trip diaries, are resource-intensive and prone to recall bias. This study presents a stop-level activity classification framework for round-trip carsharing in Bangkok, Thailand, integrating GPS trajectories, trip metadata, land use, and proximity to points of interest (POIs). Ground truth was obtained through structured phone interviews with 217 users within 48 h of trip completion. Three machine learning models—Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were evaluated, with XGBoost achieving the highest overall accuracy of approximately 63%. The model performed particularly well in identifying activities with distinct spatial–temporal patterns, including Personal Errands and Escort Activities and Outdoor & Leisure with accuracies of around 70%. Feature importance analysis indicated that stop duration, trip extent beyond Greater Bangkok, and POI density were key predictors. These results can support operators and planners by providing insights into how users interact with different locations, helping to inform service planning, pricing strategies, and infrastructure allocation. By focusing on developing urban markets in Southeast Asia, the study contributes regionally grounded evidence to the broader literature on shared mobility, digital data collection, and sustainable transport.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706458</guid>
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    <item>
      <title>Analysis of the effects of urban area expansion owing to railway development in Asian megacities using optical satellite data</title>
      <link>https://trid.trb.org/View/2706176</link>
      <description><![CDATA[For transit-oriented development (TOD) policy, understanding the relationship between urban railways and urban development is essential. In many Asian megacities, limited time-series land-use data impedes such analysis. This study introduces a rigorous method for assessing land-use changes every 30 x 30 m square area using optical satellite data, incorporating techniques to minimize errors and focus on physically developable land along railway corridors. Applying this method, a time-series analysis of Bangkok (2004–2023) across 41 zones—categorized by direction and distance—reveals that urbanization patterns vary significantly by location. Notably, developed areas along railway corridors expanded more rapidly than the metropolitan average, indicating a strong correlation between railway development and urban growth. Furthermore, the timing of development differs by section: in the purple line (Section I), urbanization accelerated post-construction, whereas in Section II, it increased following planning approval. These insights provide evidence to help policymakers optimize the timing and spatial focus of TOD initiatives.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706176</guid>
    </item>
    <item>
      <title>Assessing vehicle-to-grid integration for carsharing fleets using empirical data and simulation</title>
      <link>https://trid.trb.org/View/2683294</link>
      <description><![CDATA[Electric vehicles (EVs) are increasingly adopted to decarbonize transportation and support renewable energy integration. Vehicle-to-grid (V2G) technology enables bidirectional energy flow, enhancing grid flexibility through demand response. Carsharing fleets, characterized by shared infrastructure and extended idle times, present unique opportunities for V2G but remain underexplored. Here we analyze empirical data from 135 EVs in a Thai carsharing fleet using Monte Carlo simulations to evaluate V2G impacts under various charging schedules and tariff designs. Results indicate that, on average, a V2G discharge tariff within the range of 27-30 THB/kWh ($0.81-0.90 USD/kWh) is needed for companies to become profitable considering a baseline ratio of three cars per charger. However, if shared charging is utilized, with 17-23 cars per charger, this tariff may be reduced to 3.5-5.0 THB/kWh ($0.10-0.15 USD/kWh). Furthermore, competitive rates may also be achieved when charging infrastructure costs halve. Additionally, V2G participation could avoid 239–678 kg CO2-equivalent emissions per vehicle annually, significantly lowering lifecycle CO2 emissions. These findings suggest that targeted tariff policies and infrastructure cost reductions are critical to unlocking V2G benefits for carsharing systems and advancing sustainable urban mobility.]]></description>
      <pubDate>Thu, 30 Apr 2026 11:27:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683294</guid>
    </item>
    <item>
      <title>Discrete choice modeling for the proposed canal boat transit in western Bangkok, Thailand</title>
      <link>https://trid.trb.org/View/2664107</link>
      <description><![CDATA[The proposed canal boat transit (CBT) system, designed to connect metro rail and bus services in western Bangkok, was initiated by an academic institution in collaboration with local communities and stakeholders from both the public and private sectors. The initiative aims to support decarbonization efforts and promote sustainable urban mobility. A stated choice experiment explicitly incorporating the waterborne mode was conducted, and a transport mode choice model was estimated using sampling weights on combined revealed and stated preference (RP and SP) survey data through the nested logit trick. The results reveal that, for the proposed CBT mode, the value of time is highest for first-mile travel time (FMTT) at 22.36 (100.82) baht/hour, followed by wait time (WT) at 19.49 (42.79) baht/hour, and in-vehicle travel time (IVTT) at 1.76 (6.79) baht/hour for the low-income (high-income) group. Mode share comparisons before and after the introduction of CBT indicate slight declines across all existing modes, ranging from 0.02% to 0.04%. The estimated elasticities suggest that a 1% reduction in total cost (TC), FMTT, WT, and IVTT would increase CBT demand by 1.9670%, 0.8249%, 0.3173%, and 0.1269%, respectively. A 1% decrease in any CBT attribute would reduce public transit use more than private vehicle use. Simulations were performed to estimate mode share variations across income groups under different levels of CBT attribute reductions. Targeted incentives may integrate fare reductions to enhance affordability, connectivity improvements to improve accessibility, and community-based support programs to attract both lower- and higher-income users and reduce private car dependence.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664107</guid>
    </item>
    <item>
      <title>Spatio-temporal characteristics of taxis in Bangkok, Thailand, across multiple pandemic waves</title>
      <link>https://trid.trb.org/View/2687036</link>
      <description><![CDATA[This research investigates the impact of COVID-19 on taxi trends in Bangkok, Thailand, using data from 7 million conventional taxi trips over a four-year period—encompassing pre-pandemic, pandemic, and post-pandemic periods—by a non-negative Tucker decomposition approach. According to the decomposition results, the fourth-order taxi tensor of size [1527 × 1527 × 24 × 48] could be divided into a core tensor and four factor matrices, with the optimal ranks of 30, 30, 3, and 3 for H3 index of origins, H3 index of destinations, operating hours (time of day), and operating months, respectively. In terms of temporal dimensions, trip patterns of taxis dynamically change as COVID-19 situation develops, but there is no distinction between taxi trips on weekdays and weekends. Regarding spatial dimensions, the majority of taxi trips are short-distance, as most of them originate and destine within the same areas. Tucker decomposition also highlights a marked increase in trips between transportation hubs and healthcare facilities during morning hours, underscoring the role of taxis as a vital transportation mode for accessing essential services during the pandemic. The authors expect that, with more data availability, a deeper understanding of urban mobility patterns would be better comprehended; and, this piece of information would, in turn, allow policy makers to make informed decisions concerning the development of more sustainable urban transportation considering both the roles of transportation modes and the changes in ridership’s behavior concurrently.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687036</guid>
    </item>
    <item>
      <title>Revealing preferences for Bangkok Metro Station access modes through perceptions of safety, walkability, and service quality: A hybrid choice modeling approach</title>
      <link>https://trid.trb.org/View/2655804</link>
      <description><![CDATA[In developing cities, the effectiveness of urban rail systems depends not only on network expansion but also on safe and reliable first- and last-mile connections. This study examines metro station access mode choice in Bangkok, where informal services such as motorcycle taxis and Songthaews are widely used. A hybrid choice modeling framework is employed to integrate latent perceptions with observed factors, linking preferences, socio-demographics, and access mode choice behavior. Incorporating latent variables improved model fit, with the hybrid choice model outperforming a multinomial logit model benchmark. Walking increases when pedestrian environments meet safety and comfort expectations, while motorcycle taxi use declines when safety and service concerns persist. Songthaew choice is influenced more by tangible attributes than by latent perceptions. Socio-demographics affect mode choices both directly and indirectly through latent preferences, revealing attitudinal mediation pathways. The study highlights targeted interventions to improve walking environments, enhance motorcycle taxi safety, explore safer flexible alternatives, and standardize operations for informal modes, thereby supporting safety, sustainability, and equitable metro access in developing cities.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655804</guid>
    </item>
    <item>
      <title>Health risk assessment of BTEX exposure among public transport commuters in Bangkok, Thailand</title>
      <link>https://trid.trb.org/View/2688675</link>
      <description><![CDATA[Traffic-related volatile organic compounds (VOCs) represent an important exposure pathway for urban commuters in megacities such as Bangkok, Thailand. This study quantified personal exposure to benzene, toluene, ethylbenzene, and xylene isomers (BTEX) and evaluated associated inhalation health risks across five major transportation modes: air-conditioned bus (A/C bus), non-air-conditioned bus (non-A/C bus), taxi, the Bangkok Mass Transit System (BTS), and the Metropolitan Rapid Transit (MRT). Personal air monitoring was conducted during wet and dry seasons to capture seasonal variability in exposure. BTEX concentrations differed significantly by transport mode and season. Non-A/C buses consistently exhibited the highest concentrations, followed by A/C buses and taxis, while the lowest levels were observed in rail-based systems (BTS and MRT). Across all modes and seasons, concentrations ranged from 6.1–41.4 µg/m³ for benzene, 19.1–128.6 µg/m³ for toluene, 1.9–23.7 µg/m³ for ethylbenzene, 10.3–50.3 µg/m³ for m,p-xylenes, and 2.3–8.8 µg/m³ for o-xylene, with consistently higher levels during the dry season. Non-carcinogenic risk assessment indicated hazard index values below unity for all transport modes, with the highest value observed in non-A/C buses during the dry season (HI = 7.94 × 10⁻²). In contrast, benzene-related incremental lifetime cancer risks frequently exceeded the U.S. EPA lower benchmark (1.0 × 10⁻⁶) but remained below the upper tolerable limit (1.0 × 10⁻⁴), with the highest risk estimated for non-A/C bus commuters (9.80 × 10⁻⁶). These results demonstrate that transportation mode and seasonality are key determinants of commuter BTEX exposure and highlight the need for targeted mitigation strategies focusing on ventilation design, emission control, and cleaner public transport systems.]]></description>
      <pubDate>Tue, 07 Apr 2026 09:16:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688675</guid>
    </item>
    <item>
      <title>Study on installation of speed humps as traffic safety measures on residential roads in Bangkok</title>
      <link>https://trid.trb.org/View/2683048</link>
      <description><![CDATA[Speed humps are widely used on residential roads in Bangkok, yet their installation location and conditions are inconsistent. This study (i) conducted a wide-area survey of the speed humps using Google Street View, (ii) identified high-risk residential roads using probe density (proposed in this study) and traffic accident data, and (iii) evaluated speed control effects using 85th-percentile speed profiles around humps and between humps. This research analyzed 2414 humps in two districts in Bangkok and identified visible damage in 514 of the units. Along 39 selected high-risk roads with 189 humps, the analysis revealed that driving speeds crossing the 179 humps were consistently ≤30 km/h, however, middle-block speeds between humps frequently exceeded 30 km/h, especially on wider roads. Based on these findings, an area-based guideline was proposed to prioritize target zones, diagnose existing conditions, plan installations with consideration of road width and spacing, and evaluate outcomes to inform future improvements.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683048</guid>
    </item>
    <item>
      <title>Enhancing urban accessibility with railway network development: a comparative scenario analysis in Bangkok, Thailand</title>
      <link>https://trid.trb.org/View/2658643</link>
      <description><![CDATA[In many developing cities, urban-rail transit systems are planned under long-term master plans but often lack detailed evaluations of how network expansion changes accessibility by public transport. This study assesses the impact of railway network expansion using a cumulative accessibility index derived from GTFS-based travel times. Three scenarios are compared: the current network, the original M-MAP plan, and the proposed M-MAP2 Blueprint. The results show that the M-MAP2 Blueprint increases citywide accessibility by approximately 10%, with 50% of facilities within 20 km of the city center reachable within 60 min. While the expanded network significantly enhances accessibility, some peripheral areas remain underserved. Lorenz curve and Gini coefficient analyses further reveal improved spatial equity, with the Gini decreasing from 0.530 (current) to 0.457 (M-MAP2 Blueprint), indicating a more balanced distribution of public transport services. These findings highlight the dual benefits of the M-MAP2 Blueprint: enhancing overall accessibility while reducing spatial disparities in service provision. The study underscores the importance of integrating quantitative accessibility and equity measures into urban-rail planning to support evidence-based, inclusive, and sustainable urban mobility strategies in rapidly developing cities.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658643</guid>
    </item>
    <item>
      <title>Adaptive Signal Control for Reducing the Effects of Queues Spillback</title>
      <link>https://trid.trb.org/View/2669855</link>
      <description><![CDATA[Urban traffic congestion at signalized intersections leads to severe delays, fuel consumption, and environmental impacts, particularly in high-density areas. This study focuses on the Rama 4 intersection in Bangkok, where closely spaced intersections exacerbate queue spillback. Traditional fixed-time signals fail to adapt to fluctuating traffic volumes, necessitating an advanced approach. This research evaluates an Adaptive Signal Control (ASC) scheme using VISSIM and VISVAP simulations to manage real-time traffic conditions. Key performance metrics, including queue length, spillback events, travel time, and throughput, were analyzed across multiple ASC scenarios. Results indicate significant improvements, with ASC reducing queue lengths, spillback events, and travel time while enhancing intersection efficiency. Scenario 4, incorporating ASC with queue detection at 60 meters, proved most effective. These findings underscore ASC's potential as a scalable solution for alleviating congestion and optimizing urban intersection performance.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669855</guid>
    </item>
    <item>
      <title>Estimating the Critical Gap for U-Turns at Uncontrolled Median Openings</title>
      <link>https://trid.trb.org/View/2669836</link>
      <description><![CDATA[Critical gap is an essential traffic parameter for assessing the capacity of U-turning vehicles at uncontrolled median openings and plays a vital role in U-turn facility design. Inaccurate driver gap acceptance data in U-turn designs can lead to traffic congestion and increase the risk of severe accidents. This study aimed to estimate the critical gap for U-turns at uncontrolled median openings by analyzing real-world traffic behavior across three distinct U-turn patterns in Bangkok using various estimation methods. The results showed that the Raff method produced the lowest critical gap estimates during both peak and off-peak hours (3.24 to 5.39 seconds), followed by the Maximum Likelihood Estimation method (4.29 to 6.69 seconds), the Greenshield method (4.75 to 6.75 seconds), and the Acceptance Curve method, which yielded the highest estimates (6.75 to 9.30 seconds). The findings also revealed that as direct traffic volume increased, the estimated critical gap values from all methods tended to decrease, and differences in U-turn patterns influenced the critical gap values. Among the methods evaluated, the Maximum Likelihood Estimation method demonstrated the highest consistency in critical gap estimation under varying traffic conditions.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669836</guid>
    </item>
    <item>
      <title>How Travel Time, Cost, and Attitudes impact Travel Behaviour in Developing Countries: A case study in Bangkok, Thailand</title>
      <link>https://trid.trb.org/View/2646021</link>
      <description><![CDATA[As travel behaviour in developing countries remains relatively unexplored, the present study aims to understand attitudes towards the environment, accessibility, convenience, and safety and their influence on individual travel behaviour. A questionnaire was designed and executed to generate a sample of 648 respondents from the Bangkok Metropolitan Region, between April and May 2021, and subsequently analysed. The results showed that attitudes were influenced by a combination of demographic factors (gender and age), travel related attributes including cost and time, and mode of transport used. Age and transport mode were found to be particularly influential in determining priorities for safety, environment and health, and accessibility, while gender differences are most evident in accessibility preferences. These findings highlight the importance of considering a range of demographic and behavioural factors when designing policies or interventions to address travel attitudes, to meet user needs and to promote sustainable transport systems.]]></description>
      <pubDate>Thu, 12 Mar 2026 16:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646021</guid>
    </item>
    <item>
      <title>Prehospital Care of Road Traffic Injuries in Chiang Mai, Thailand</title>
      <link>https://trid.trb.org/View/2635334</link>
      <description><![CDATA[Road traffic accidents (RTAs) cause enormous morbidity and mortality in developing countries. The Global Burden of Disease Study projected that RTAs will be the third highest cause of disability adjusted life years (DALYs) by 2020. Ninety percent of the DALYs due to RTAs are in developing countries. Because the majority of trauma deaths in developing nations occur in the prehospital setting, it is imperative that emergency medical systems be established and improved in such countries. Two studies in Central America found that increasing the number of emergency dispatch units and prehospital personnel training increased the utilization of emergency medical devices and lowered the percentage of patients who die en route to the hospital. RTA fatalities are rising faster in Asia than anywhere else in the world. This is the report of a field study conducted between May and June 2003 in Chiang Mai and Bangkok. Information was gathered through oral interviews and written questionnaires and was supplemented by publications of The Narenthorn EMS Center of the Ministry of Health and unpublished documents of the Chiang Mai Health Department Office of Emergency Care. The research objectives were: (1) to learn how emergency rescue services are organized in Chiang Mai, (2) to learn about ongoing public health efforts to improve such services, and (3) to learn about the training, certification, employment and medical device usage of prehospital personnel. There was no specific a priori hypothesis.]]></description>
      <pubDate>Sat, 03 Jan 2026 17:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635334</guid>
    </item>
    <item>
      <title>Analyzing the travel impacts of an optimal congestion charge with a multimodal network equilibrium model for Bangkok</title>
      <link>https://trid.trb.org/View/2633825</link>
      <description><![CDATA[This paper presents the forecasted travel impacts according to a congestion charging scheme if launched in Bangkok. The results show the consequences at different levels of charging values. The authors implement a transportation network equilibrium model for urban road and rail networks with a road congestion charge to calculate the optimal zonal congestion charge for the city. A congestion charge is sought to maximize the surplus comprising all commuters and government benefits. The model estimates the travel with the times by mode at the level of detail used by transportation planning organizations. The car traffic assignment model of the road and expressway network is solved. The transit assignment model is solved with fixed routes with fixed headways and unknown departure times. The transit route choice model adopts a frequency-based assignment with a route-finding algorithm using the network constructed especially for this study. The transit problem is formulated as an optimization problem using an optimal strategy called the common-line problem. Travelers are assumed to conform to an elastic-demand user equilibrium traffic assignment, corresponding to the data. This paper analyzes the scale of the decrease in the car usage and switching proportions to the other travel modes compared to one in the absence of a congestion charge. It is found that the charge yielding the maximum government revenue is roughly 3.6 times the charge yielding the largest social surplus for the given input conditions.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633825</guid>
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