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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>Impact of Bus Rapid Transit System on Suburban Bus Users in Chennai</title>
      <link>https://trid.trb.org/View/2727684</link>
      <description><![CDATA[This study assesses the prospective effects of the proposed Bus Rapid Transit (BRT) system on current Metropolitan Transport Corporation (MTC) bus service along the Poonamallee-Sunguvarchatram corridor in the western suburb of Chennai. The corridor, strategically linking the existing Chennai Airport located at Meenambakkam and the proposed Parandur Greenfield Airport, is expected to experience an increased travel demand in the near future. It is a well-known fact that a BRT system has better speed compared to a bus in a mixed-traffic flow condition. In this background, to assess the attractiveness of BRT from Poonamallee to Sunguvarchatram, a Stated Preference (SP) survey was conducted among 430 bus users. Binary logit models for two cases were developed: (1) full data set and (2) for trips < 20 km. The model results indicated that the travel time savings and the proposed ‘fare difference’ were significantly influencing the modal shift. When the BRT fare is 1.5 times the express bus fare, the expected shift probabilities are significantly high (89–98%). Small-scale business users, private-sector employees, and younger commuters exhibit lower preference for the proposed BRT mode. Both the models demonstrated strong explanatory power (ρ² ≥ 0.50). The study also finds that an electrically driven BRT system can reduce CO2 emissions by 1.815 tonnes/day by 2050 and improve journey speeds from 21 km/h to 27 km/h (at least for the initial years). Overall, the study confirmed that a well-designed BRT system with affordable fares can significantly enhance mobility, sustainability, and public transport efficiency along the study corridor.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727684</guid>
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    <item>
      <title>Toll-Plaza Design: A Case Study of Bengaluru-Chennai Highway</title>
      <link>https://trid.trb.org/View/2659276</link>
      <description><![CDATA[This paper provides a methodology for the design of a toll plaza as per Indian Roads Congress (IRC) guidelines. For this study Nemili toll plaza located on Bengaluru–Chennai highway (NH-48) was chosen. Relevant secondary data pertains to the year 2022 were collected from the National Highway Authority of India (NHAI), Chennai regional office. For the toll able category of vehicles, Average Daily Traffic (ADT) was estimated for the base year 2022. A suitable Seasonal Correction Factor (SCF) was assumed (usually calculated from petrol/diesel sales data) in order to estimate the Annual Average Daily Traffic (AADT). Population, per-capita income and net state domestic product data for the influencing states were collected in time-series and using the suitable state influencing factor, traffic growth elasticity values were arrived. Using the traffic growth elasticity values, base year traffic (i.e. AADT) was projected to the design year 2037. Since stage construction is practiced in India, number of electronic toll bays required for the design year 2030 was calculated based on the traffic corresponding to that year referring the IRC codal recommendations.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:38:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659276</guid>
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    <item>
      <title>Optimizing Transit-Oriented Development Strategies Integrating Land Value Capture in Chennai’s Metro Corridor</title>
      <link>https://trid.trb.org/View/2648495</link>
      <description><![CDATA[Cities face challenges such as pollution, traffic congestion, and rising land costs resulting from rapid urbanization. Transit-Oriented Development (TOD), an integrated approach to land use and transportation, has emerged as a promising solution to create walkable communities with high-density developments near transit hubs. At the same time, it is also important to assess the redevelopment potential and value gains from such developments. In this regard, the study formulates a framework to identify the potential land parcels for redevelopment and amalgamation, along with a suitable value capture mechanisms in the case of Chennai, India. Indicators were identified in accordance with international best practices. Residents’ perspectives on TOD awareness, willingness to redevelop, and suitable Land Value Capture (LVC) mechanisms were recorded. Findings reveal limited public awareness about TOD and associated value capture mechanisms, as well as fragmented land ownership that limits redevelopment potential. Interviews with developers and decision-makers revealed challenges such as developers’ reluctance toward TOD due to inadequate incentives and financial constraints that hinder further development. To address these issues, a spatial index is developed and refined through a Principal Component Analysis (PCA) to evaluate land transformation potential using factors such as building height, plot size, accessibility, and ownership patterns. The index can be used to identify and rank plots suitable for redevelopment and amalgamation, and can be replicable for other cities. Based on the index outputs, policy variables such as Floor Area Ratio (FAR) and the LVC mechanism combinations can be tested to optimize development in TOD zones.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648495</guid>
    </item>
    <item>
      <title>Stop-and-Go Trajectory Data of Disordered Traffic Using a Swarm of Unmanned Aerial Vehicles</title>
      <link>https://trid.trb.org/View/2572520</link>
      <description><![CDATA[Studying traffic dynamics and behaviour requires detailed information on vehicular trajectories. However, vehicle trajectory extraction is challenging in congested and disordered traffic due to false detections and tracking errors caused by closely spaced platoons and vehicle occlusion. Unmanned aerial vehicles (UAV) give a bird-eye view of traffic from which vehicular trajectories can be effectively extracted. The most studies employed a single UAV to capture data, limiting the trajectory information to small road segments, which may not reflect the complete traffic dynamics and naturalistic driving behaviour. This study aims to collect stop-and-go traffic data in disordered traffic conditions over a longer road section using a swarm of UAVs. The data were collected on a six-lane divided urban arterial road in Chennai city, India. Furthermore, the study presents a process for collecting vehicle trajectory data from video feeds of a swarm of UAVs. A pre-processing method based on feature transformation for unifying frames from individual UAVs covering a long road section has also been developed. A framework for effectively detecting vehicles in large pixel frames at congested states has been proposed using a supervised learning-based method and the Slicing Aided Hyper Inference algorithm in a pipeline. An observation-centric tracking algorithm was employed to mitigate the tracking errors. Detailed performance evaluation of the presented framework has been done on the collected dataset. The results show effective detection and tracking of vehicles in congested and disordered traffic, giving high-precision vehicle trajectories.]]></description>
      <pubDate>Mon, 15 Dec 2025 10:32:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572520</guid>
    </item>
    <item>
      <title>DAEENet: A novel Dual Attention based Edge Enhanced network for asphalt pavement deterioration detection and segmentation</title>
      <link>https://trid.trb.org/View/2618184</link>
      <description><![CDATA[Accurate identification of asphalt pavement deterioration is essential for ensuring road safety and optimizing infrastructure maintenance. Traditional methods for detecting pavement distress are often inefficient, time-consuming, labor-intensive, and lack the precision required for timely decision-making. These outdated techniques not only increase operational costs but also delay necessary interventions, potentially leading to more severe damage and safety hazards. To address these challenges, a novel deep learning model is proposed, namely Dual Attention-based Edge Enhanced Network (DAEENet), designed to automatically and precisely detect and segment various types of pavement distress. DAEENet has three sub-networks: Dual Local Channel Attention (DLCA), Edge Enhancement Attention (EEA), and Crack Severity Analysis (CSA). The DLCA mechanism, applied within an encoder framework, effectively extracts complex crack patterns while minimizing background interference. The EEA enhances boundary segmentation through spatial and channel attention mechanisms, improving the accuracy of crack detection. Crack severity is then assessed using morphological operations, skeletonization, distance transform, and convex hull analysis, categorizing cracks into low, medium, or high severity levels. Our proposed model achieves an F1 score of 87.5%, IoU of 84.1%, and a Dice coefficient of 87.5%. These metrics consistently outperform current state-of-the-art approaches.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:31:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618184</guid>
    </item>
    <item>
      <title>Modal Shift Analysis of Bus Passengers Towards Metro Rail: A Mathematical Approach</title>
      <link>https://trid.trb.org/View/2601452</link>
      <description><![CDATA[This study aims to explore the causal factors which induce present bus users’ shift towards the proposed metro rail in Chennai. The study corridor is from Poonamallee to Sriperumbudur having 21 km length and is part of Chennai – Bengaluru Industrial Corridor (CBIC) where many industries and educational institutions are located. To estimate the probability shift, a Stated Preference (SP) questionnaire was prepared stating the following fare scenarios: Metro fare as 1.5 times the express bus fare; 2 times the express bus fare; 2.5 times the express bus fare; & 3 times the express bus fare. The above scenarios were arrived based on comparison of express bus fare and metro fare in Chennai. Random sample technique was used for the SP survey and 300 bus travelers were interviewed. Collected data was binary coded and a binary logit model was developed using Statistical Software Tools (SST) software. Binary logit model serves satisfactorily for the selected study corridor identify crucial factors that can be used for metro planning. From this study, it was concluded that the proposed fare played a vital role in deciding the shift. Shift estimates indicated that around sixty percent of bus passengers will turn to metro if the metro fare is fixed twice the express bus fare, whereas bus travelers with annual income below 5 lakhs & aware of metro proposal were less likely to use metro. The estimated shift values will help metro authority to establish a new fare policy.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:31:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601452</guid>
    </item>
    <item>
      <title>Data-Driven Analysis of Run-level Bus Alighting Patterns for Accurate Predictions and Operational Efficiency</title>
      <link>https://trid.trb.org/View/2566077</link>
      <description><![CDATA[Accurate prediction of bus passengers alighting at each stop during each run is crucial to improve ridership and implement destination-specific demand-responsive solutions like last-mile connectivity (LMC) and multimodal transfers. However, existing studies often disregard empirical distribution, heterogeneity, and heteroscedasticity of alighting patterns at disaggregate run-level resolution. Machine learning (ML) models, although capable of modeling complex relationships, are often replaced by traditional methods due to interpretability needs in policymaking. Therefore, this paper investigates factors influencing bus alighting behavior using ML techniques, namely decision tree, random forest, and gradient boosting, and compares against a traditional OLS model to evaluate prediction accuracy, variable impacts, and robustness at the run level with electronic ticketing data from Chennai, India. Results show that gradient boosting outperforms OLS with a 51% improvement in MAE (from 2.50 to 1.23) and a 41% improvement in RMSE (from 3.76 to 2.21), respectively. It also effectively addresses zero inflation, heterogeneity, and heteroscedasticity without data transformation. Passenger occupancy, a real-time operational variable, explained 29% of total variability, capturing dynamic conditions that static variables cannot. The ‘neighborhood attraction points’ category accounts for 6.5% of the total variation, revealing trip-chaining and substitution effects not only at the current but also preceding and subsequent stages. Coarser resolution data (e.g., hourly/daily) cause inaccurate generalizations and reduce variables’ significance, emphasizing the need for finer run-level resolution for robust predictions. A practical application of the proposed model demonstrates that increasing ordinary bus services from the current 7% to 9% optimizes ridership by balancing enhanced users’ affordability with minimized revenue loss for transit operators.]]></description>
      <pubDate>Fri, 29 Aug 2025 10:03:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2566077</guid>
    </item>
    <item>
      <title>To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts</title>
      <link>https://trid.trb.org/View/2548170</link>
      <description><![CDATA[The existence of a hybrid relationship connecting departure delay and resulting en-route conflicts remains a significant gap in the air transportation literature. This paper aims to establish a hybrid relationship to quantify the impact of departure delays on both the probability of en-route conflicts and the number of such conflicts. The proposed relationship is modelled using a binomial logistic regression framework, where the dependent variable, referred to as the conflict outcome at an en-route waypoint, is binary. It takes one of two possible levels: Conflict or No Conflict. The independent variables in the model are departure delay and the number of waypoints crossed since take-off. By utilising the logistic regression equation and assuming a binomial distribution for the number of conflicts, the model computes the expected number of conflicts at any given en-route waypoint. The logistic regression model parameters are estimated using maximum likelihood estimation, utilising data obtained from the Flight Data Processing System at Anna International Airport, Chennai, India. The relationship is then validated for multiple scenarios. Results and findings reinforce the hypothesised relationship, indicating an increase in delay and the number of waypoints crossed corresponds to higher conflict probabilities. Moreover, the impact of delay on conflict likelihood increases with an increase in the number of waypoints crossed. The hybrid relationship demonstrates flexibility and sufficient generalizability to account for multi-airport delay scenarios. Validation tests do not show any statistically significant difference between the observed conflict counts and the expected values predicted by the proposed relationship. Further post-estimate analyses confirm the robustness and stability of the relationship parameters. The primary recommendations hint towards the potential integration of this relationship into system-level delay management frameworks, encompassing both optimal departure and arrival management strategies. When incorporated, the proposed relationship enables the possibility of pragmatic, conflict-aware departure and arrival management.]]></description>
      <pubDate>Thu, 10 Jul 2025 16:38:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548170</guid>
    </item>
    <item>
      <title>Spatio-Temporal Machine Learning Approaches for Bus Travel Time Prediction</title>
      <link>https://trid.trb.org/View/2543672</link>
      <description><![CDATA[Predicting bus travel times is critical for enhancing passenger satisfaction, optimizing route planning, and improving overall system efficiency. This study explores spatio-temporal modeling techniques to predict travel times for a bus route in Chennai, India, leveraging spatial dependencies across route sections and temporal variations in travel conditions. Machine learning algorithms, including Weighted K-Nearest Neighbors (KNN) and clustering-based approaches, were compared with deep learning methods like Recurrent Neural Networks (RNN). The models incorporated handcrafted spatio-temporal features and real-time data to improve prediction accuracy. The results reveal that machine learning models with spatio-temporal feature engineering performed competitively with deep learning models utilizing real-time data. The study presents the strengths and limitations of each approach, offering insights into effective spatio-temporal modeling for bus travel time prediction.]]></description>
      <pubDate>Wed, 28 May 2025 17:00:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543672</guid>
    </item>
    <item>
      <title>Chennai’s Urban Mobility Transformation</title>
      <link>https://trid.trb.org/View/2536187</link>
      <description><![CDATA[Chennai, the capital of Tamil Nadu, is the fourth largest city in India and a hub for commerce, culture, and education. This compendium explores how cities can address complex urban mobility challenges using the city of Chennai, India as an example. The compendium showcases Chennai’s achievements in laying the foundation for improved mobility while also discussing existing and emerging challenges. In doing so, the compendium aims to stimulate dialogue and offer insights that policymakers and practitioners can use for tackling similar challenges in their cities.]]></description>
      <pubDate>Thu, 24 Apr 2025 16:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536187</guid>
    </item>
    <item>
      <title>Travel Time Reliability Prediction Using Quantile Random Forest Regression</title>
      <link>https://trid.trb.org/View/2526246</link>
      <description><![CDATA[The prediction of travel time is of paramount interest to the planning, design, operations, and management of any transportation facility. While the average travel time provides an idea of how long a trip will take, it does not provide information on its reliability. In contrast, percentiles provide more detailed information on reliability by determining the range of travel times that can be expected for a given trip. Thus, the prediction of travel time percentiles helps in travel time reliability studies. In this study, the use of Quantile Random Forest (QRF) Regressor is used to predict travel time percentiles. QRF is a flexible machine learning algorithm that can capture the complex relationships between predictor variables and the response variable. The study uses Wi-Fi sensors based data collected from Rajiv Gandhi IT Expressway in Chennai. The performance of the QRF model is evaluated using mean absolute percentage error (MAPE). The results show that the QRF model performed well in predicting travel time percentiles, with the best performance observed for the median percentile. Thus, the QRF model can provide accurate and reliable travel time predictions, which can be used by transportation planners and traffic engineers to optimize traffic flow and improve transportation efficiency.]]></description>
      <pubDate>Tue, 22 Apr 2025 15:51:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526246</guid>
    </item>
    <item>
      <title>Ground Improvement by Prefabricated Vertical Drains and Surcharge for a Metro Depot Constructed on Marine Deposit</title>
      <link>https://trid.trb.org/View/2522042</link>
      <description><![CDATA[This paper focuses on field measurements, as well as analytical and numerical analyses conducted for a metro depot in Chennai, India, that employs prefabricated vertical drains (PVD) in conjunction with surcharge. The PVDs were installed in a triangular arrangement and extended to the bottom of the soft clay layer. Two PVD spacings of 1.2 m and 1.5 m were selected based on the site zone. Three surcharge fill heights—2.2 m, 3.0 m, and 3.6 m—were utilized, with the soil having a unit weight of 17 kN/m³. The surcharge was removed after 125 days, resulting in a maximum average settlement of approximately 150 mm. The monitoring data and analytical calculations confirmed the completion of primary consolidation. Both numerical simulations using commercial software and an analytical approach based on settlement and flow variables were employed to evaluate the scheme’s effectiveness. The analysis showed that the soft marine clay exhibited a high horizontal drainage capacity (C[subscript r]/C[subscript v] ratio between 4 and 22), which led to rapid settlement at the project site. Field measurement data were back-calculated to derive the consolidation settlement parameters. These findings could significantly impact future designs for ground improvement in the coastal region of South India.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522042</guid>
    </item>
    <item>
      <title>Examining bike share usage trends in Metropolitan cities through App based data. A case study of Chennai, India</title>
      <link>https://trid.trb.org/View/2493027</link>
      <description><![CDATA[Bicycle-sharing systems (BSSs) have emerged as an important climate-smart transportation strategy in many cities, supporting sustainable low-carbon societies. Despite becoming permanent fixtures in the Western urban landscape, bike share implementation in Asia has been disappointing, apart from China. However, future bike share growth trends indicate that Asian cities are potential hubs for bike share schemes. As a result, it is critical to investigate the current operations of bike-share schemes in these cities to understand the factors influencing bike-share use in various urban areas, which can improve system performance and encourage more use in the future. This paper investigates usage trends in a bike-sharing scheme that has been in operation in Chennai since 2019. While many studies have been conducted on how bike-sharing schemes are changing mobility in cities around the world, particularly in developed countries, few have addressed the dynamics of these schemes in cities in developing countries such as India. One of the reasons for looking at a city like Chennai is to see if metropolitan cities in developing countries benefit from bike-sharing schemes and if bike-sharing schemes can play a prominent role in these cities, as these cities face many transportation challenges such as congestion, delay, pollution, accidents, and last mile connectivity issues, among others. The study used app data to conduct an exploratory analysis to examine the impact of factors such as temporal, weather, travel characteristics, and bike type (conventional or E-bike) on bike share usage in the city. The MNL model results from this study related to bike-share ride duration (short, medium, and long) can help other cities in developing countries improve and/or expand their existing bike-share networks, as well as cities planning to launch new bike-share programs. Furthermore, examining the impact of different temporal, and weather factors on mode usage behaviour (conventional and e-bike) in bike share provides insights into user preferences and how these factors influence user mode choice behaviour in similar cities.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493027</guid>
    </item>
    <item>
      <title>Pedestrian Injury Severity Modelling with Additional Secondary Data Extracted using Satellite Images</title>
      <link>https://trid.trb.org/View/2492899</link>
      <description><![CDATA[Pedestrian-vehicle crashes are a leading cause of road traffic crash death all over the world. Since the factors affecting the crashes depends on many factors such as road infrastructure and environmental settings, determining area-specific characteristics which influence the crashes is important. This study focuses on pedestrian crash severity modelling in Chennai, India. The authors develop an ordered probit model to identify the factors affecting different levels of injury severities for the years 2017- 2019. The severity levels were divided into three categories, simple injury, grievous injury, and fatality. The primary data was taken from Road Accident Database Management System and additional data about location-specific information such as intersection presence, type of intersection and bus stop presence were extracted manually using Google Earth Pro. The results showed that environmental settings (urban or rural) significantly impact pedestrian-vehicle crashes with higher injury severities in rural areas. Time of the day, presence of bus stops within 250ft of crashes and intersection-related crashes also have significant impacts on the severity. Gender related factors were explored. Further interpretation of results was done by computing the marginal effects. Site specific treatments and need for advance pedestrian safety measures are highlighted after analysing the modelling results.]]></description>
      <pubDate>Mon, 10 Feb 2025 09:32:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2492899</guid>
    </item>
    <item>
      <title>A Simulation Model for Suburban Railway to Study the Effect of Capacity and Headway on Waiting Time</title>
      <link>https://trid.trb.org/View/2264079</link>
      <description><![CDATA[In this research work a simulation model is developed to study the effects of change in frequencies or capacity of suburban trains on waiting time for the given demands at various suburban railway stations. As a case study Madras Beach — Tambaram meter gauge suburban railway line in Chennai, India was taken. The simulation model is used to find optimum frequency for existing and enhanced capacity of trains for desired loading level.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264079</guid>
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