Highlights The probability of traffic conflicts was studied for the lane-shifting sections during the highway reconstruction and expansion activities. To accurately identify the traffic conflict in this complex road section, this study adopted the indicator pair of TTC and TDTC instead of TTC alone. The correlations between the road and traffic factors and the vehicle collision risk were evaluated via machine learning. The influencing factors were found to be the curve radius, the standard deviation of single-vehicle speed, the cumulative angle in unit length, the vehicle type, the average speed, and the standard deviation of traffic flow speed in decreasing order of their influence on the probability of traffic conflicts. Based on the conclusions, pertinent measures can be taken to reduce the collision risk of vehicles in the lane-shifting sections. Specific proposals are: diversion of large vehicles; restriction of the vehicle speed in the lane-shifting sections; increase of the curved section radius in line with the specifications; decrease of the cumulative turning angle per unit length i.e. the average turning curvature per unit length.

    Abstract Introduction: There are designated sections for lane-shifting in several highway reconstruction and expansion zones. Similar to the bottleneck sections of highways, these sections are characterized by poor pavement surface conditions, disorderly traffic flow, and high safety risk. This study examined the continuous track data of 1,297 vehicles collected using an area tracking radar. Method: The data from the lane shifting sections were analyzed in contrast with the regular section data. Further, the single-vehicle attributes, traffic flow factors, and the respective road characteristics in the lane-shifting sections were also taken into account. In addition, the Bayesian network model was established to analyze the uncertain interaction between the various other influencing factors. The K-Fold cross validation method was used to evaluate the model. Results: The results showed that the model has a high reliability. The analysis of the model revealed that the significant influencing factors in decreasing order of their influence on the traffic conflict are: the curve radius, cumulative turning angle per unit length, standard deviation of the single-vehicle speed, vehicle type, average speed, and the standard deviation of the traffic flow speed. The probability of traffic conflicts is estimated to be 44.05% when large vehicles pass through the lane- shifting section while it is 30.85% for small vehicles. The probabilities of traffic conflict are 19.95%, 34.88%, and 54.79% when the turning angles per unit length are 0.20 °/m, 0.37 °/m, and 0.63 °/m, respectively. Practical Applications: The results support the view that the highway authorities help reduce traffic risks on lane change sections by diverting large vehicles, implementing speed limits on road sections, and increasing the turning angle per unit length of vehicles.


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    Title :

    Traffic conflicts in the lane-switching sections at highway reconstruction zones


    Contributors:
    Zou, He (author) / Zhu, Shunying (author) / Jiang, Ruoxi (author) / Chen, Qiucheng (author) / Wu, Jingan (author) / Wang, Pan (author) / Diao, Chengliang (author)

    Published in:

    Publication date :

    2022-11-07


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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