安全视角下的交通监控:ITS数据应用

M. Abdel-Aty, A. Pande, N. Uddin
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引用次数: 2

摘要

响应式交通管理策略,如事件检测,与移动电话使用的进步越来越不相关。21世纪的高速公路管理需要将重点转向积极主动的策略,包括预测事故,如碰撞。“预测”交通事故也将是交通安全的关键。本文提出了一种通过实时交通监控数据来识别高概率碰撞的高速公路位置的两步方法。在这项研究中,从环形探测器收集了4号州际公路58公里(36英里)的走廊上的历史碰撞和相应的交通数据。在探索性分析之后,开发了两种类型的逻辑回归模型,即简单回归模型和多元回归模型。简单模型用于推断碰撞风险的时空变化模式,而多变量模型用于交通模式的最终分类。作为简单模型的建议应用,它们的输出可用于对崩溃风险的初步评估。如果有高崩溃风险的迹象,那么可以使用多变量模型显式地将数据模式分类为导致或不导致崩溃发生。文中还对该两阶段实时应用策略进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic surveillance from a safety perspective: an ITS data application
Reactive traffic management strategies such as incident detection are becoming less relevant with the advancement of mobile phone usage. Freeway management in the 21st century needs to shift focus toward proactive strategies that include anticipating incidents such as the crashes. "Predicting" crash occurrences would also be the key to traffic safety. A two-step approach to identify freeway locations with high probability of crashes through real-time traffic surveillance data is presented here. For this study historical crash and corresponding traffic data from loop detectors were gathered from a 58-km (36-mile) corridor of Interstate-4. Following an exploratory analysis two types of logistic regression models, i.e., simple and multivariate, were developed. The simple models were used to deduce time-space patterns of variation in crash risk while the multivariate model was chosen for final classification of traffic patterns. As a suggested application for the simple models, their output may be used for preliminary assessment of the crash risk. If there is an indication of high crash risk then the multivariate model may be employed to explicitly classify the data patterns as leading or not-leading to crash occurrence. A demonstration of this two-stage real-time application strategy is also provided in the paper.
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