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    Development of a Rear-End Conflict Prediction Framework for Unsignalized Intersections along Intercity Highways Using Machine Learning

    Source: Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 004::page 04025009-1
    Author:
    Barun Das
    ,
    Madhumita Paul
    DOI: 10.1061/JTEPBS.TEENG-8015
    Publisher: American Society of Civil Engineers
    Abstract: Rear-end collisions at high-speed unsignalized intersections are one of the primary causes of crash fatalities. Time to collision (TTC) has become a popular metric for assessing traffic conflicts and proactively evaluating the risk of rear-end collisions. However, in developing countries such as India, identifying rear-end conflicts utilizing a single time-based indicator could be misleading because different vehicle classes have distinct dynamic characteristics and travel at varying speeds. Consequently, this study used three proximal indicators: TTC, deceleration rate (DR), and relative speed of leader-follower vehicles to identify rear-end conflicts along intersections’ approaches from two crash-prone unsignalized junctions located in the National Capital Region, India. Trajectory profiles of each leader-follower vehicle pair extracted using a semiautomated tool were used for estimating these indicators. Rear-end conflicts were further classified into “severe” and “nonsevere” groups based on K-means clustering and indicators’ threshold values applicable to mixed traffic conditions. The results showed that the highest percentage of severe conflict was observed at a four-legged intersection compared to a three-legged one. This study further developed a rear-end conflict prediction framework to predict rear-end conflicts based on their severity classification along high-speed unsignalized intersections’ approaches using seven machine learning (ML) algorithms. Among all, the random forest (RF) algorithm performed the best for both sites. The study outcomes suggested that despite the lack of reliable crash information, a combination of surrogate safety measures and machine learning algorithms can produce reliable models for predicting rear-end conflicts in developing countries such as India.
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      Development of a Rear-End Conflict Prediction Framework for Unsignalized Intersections along Intercity Highways Using Machine Learning

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304336
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    contributor authorBarun Das
    contributor authorMadhumita Paul
    date accessioned2025-04-20T10:15:39Z
    date available2025-04-20T10:15:39Z
    date copyright1/17/2025 12:00:00 AM
    date issued2025
    identifier otherJTEPBS.TEENG-8015.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304336
    description abstractRear-end collisions at high-speed unsignalized intersections are one of the primary causes of crash fatalities. Time to collision (TTC) has become a popular metric for assessing traffic conflicts and proactively evaluating the risk of rear-end collisions. However, in developing countries such as India, identifying rear-end conflicts utilizing a single time-based indicator could be misleading because different vehicle classes have distinct dynamic characteristics and travel at varying speeds. Consequently, this study used three proximal indicators: TTC, deceleration rate (DR), and relative speed of leader-follower vehicles to identify rear-end conflicts along intersections’ approaches from two crash-prone unsignalized junctions located in the National Capital Region, India. Trajectory profiles of each leader-follower vehicle pair extracted using a semiautomated tool were used for estimating these indicators. Rear-end conflicts were further classified into “severe” and “nonsevere” groups based on K-means clustering and indicators’ threshold values applicable to mixed traffic conditions. The results showed that the highest percentage of severe conflict was observed at a four-legged intersection compared to a three-legged one. This study further developed a rear-end conflict prediction framework to predict rear-end conflicts based on their severity classification along high-speed unsignalized intersections’ approaches using seven machine learning (ML) algorithms. Among all, the random forest (RF) algorithm performed the best for both sites. The study outcomes suggested that despite the lack of reliable crash information, a combination of surrogate safety measures and machine learning algorithms can produce reliable models for predicting rear-end conflicts in developing countries such as India.
    publisherAmerican Society of Civil Engineers
    titleDevelopment of a Rear-End Conflict Prediction Framework for Unsignalized Intersections along Intercity Highways Using Machine Learning
    typeJournal Article
    journal volume151
    journal issue4
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-8015
    journal fristpage04025009-1
    journal lastpage04025009-13
    page13
    treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 004
    contenttypeFulltext
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