Real-time crash potential prediction on freeways using connected vehicle data

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Shile Zhang, Mohamed Abdel-Aty
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引用次数: 11

Abstract

The real-time crash potential prediction model is one of the important components of proactive traffic management systems. Over the years numerous models have been proposed to predict crash potential and achieved promising results using input data from roadside detectors. However, the detectors are normally installed at certain locations with limited coverage, while the connected vehicle data can provide city-wide mobility information. Previous studies have found that driver event variables such as hard braking, hard accelerations, etc. are correlated with crash potential on the road segments. Nevertheless, the existing studies are mostly conducted at the aggregated level, and the data are mostly collected from commercial vehicles such as taxis or buses traveling in the urban areas. This paper proposes a bidirectional long short-term memory (LSTM) model with two convolutional layers to predict real-time crash potential on freeways. The input data including traffic flow variables from detectors, and driver event variables from connected vehicle (CV) data, are aggregated at the one-minute level. The model achieves a recall value of 0.772 and an AUC value of 0.857. Moreover, to investigate the transferability of the proposed model, the original data are aggregated at the hourly level. The transferred model is developed with fine tuning two convolutional layers of the established model. And the transferred model achieves a recall value of 0.715 and an AUC value of 0.763. This proves that the proposed model can be successfully applied to another similar data set, or when the connected vehicles have lower penetration rate. In this study, we proved the usefulness of the connected vehicle data in the prediction of real-time crash potential, and the possibility of using it without detector data once the penetration rate increases to a reasonable level.

基于车联网数据的高速公路实时碰撞预测
碰撞潜力实时预测模型是主动交通管理系统的重要组成部分之一。多年来,人们提出了许多模型来预测碰撞的可能性,并利用路边探测器的输入数据取得了可喜的结果。然而,探测器通常安装在覆盖范围有限的特定地点,而连接的车辆数据可以提供全市范围的移动信息。以往的研究发现,硬制动、硬加速等驾驶员事件变量与路段的碰撞潜力相关。然而,现有的研究大多是在综合水平上进行的,数据大多来自于在城市地区行驶的出租车或公共汽车等商业车辆。提出了一种具有两层卷积的双向长短期记忆(LSTM)模型来预测高速公路上的实时碰撞可能性。输入数据包括来自检测器的交通流量变量和来自联网车辆(CV)数据的驾驶员事件变量,以一分钟为单位进行汇总。该模型的召回值为0.772,AUC值为0.857。此外,为了研究所提出模型的可转移性,原始数据以小时为单位进行汇总。通过对已建立模型的两个卷积层进行微调,建立了传递模型。迁移模型的召回值为0.715,AUC值为0.763。这证明了该模型可以成功地应用于其他类似的数据集,或者当联网车辆的渗透率较低时。在本研究中,我们证明了联网车辆数据在预测实时碰撞潜力方面的有用性,以及一旦普及率提高到合理水平,在没有检测器数据的情况下使用它的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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