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信号控制部分苜蓿叶式立交出口匝道潜在逆行进入情况的预测

Prediction of potential wrong-way entries at exit ramps of signalized partial cloverleaf interchanges.

作者信息

Baratian-Ghorghi Fatemeh, Zhou Huaguo, Jalayer Mohammad, Pour-Rouholamin Mahdi

机构信息

a Department of Civil Engineering , Auburn University , Auburn , Alabama.

出版信息

Traffic Inj Prev. 2015;16(6):599-604. doi: 10.1080/15389588.2014.981651. Epub 2014 Nov 6.

Abstract

BACKGROUND

Several previous studies, based upon wrong-way driving (WWD) crash history, have demonstrated that partial cloverleaf (parclo) interchanges are more susceptible to WWD movements than others. Currently, there is not a method available to predict WWD incidents and to prioritize parclo interchanges for implementing safety countermeasures for reducing WWD crashes.

OBJECTIVES

The focus of this manuscript is to develop a mathematical method to estimate the probability of WWD incidents at exit ramp terminals of this type of interchange.

METHODS

VISSIM traffic simulation models, calibrated by field data, are utilized to estimate the number of potential WWD maneuvers under various traffic volumes on exit ramps and crossroads. The Poisson distribution model was implemented without field observation and crash data.

RESULTS

A comparison between the field data and simulation outputs revealed that the developed model enjoys an acceptable level of accuracy. The proposed model is largely sensitive to left-turn volume toward an entrance ramp (LVE) than stopped vehicles at an exit ramp (SVE). The results indicated that potential WWD events increase when LVEs increase and SVEs decrease. Also, the probability of WWD event decreases as road users are more familiar with the facility.

CONCLUSION

The proposed method can diminish one of the challenges in front of transportation engineers, which is to identify high WWD crash locations due to insufficient information in crash reports. The results are helpful for transportation professionals to take proactive steps to identify locations for implementing safety countermeasures at high risk signalized parclo interchanges.

摘要

背景

先前的几项基于逆行驾驶(WWD)撞车历史的研究表明,部分苜蓿叶式(parclo)立交桥比其他类型的立交桥更容易发生逆行驾驶行为。目前,尚无一种方法可用于预测逆行驾驶事件,并确定优先需要采取安全对策以减少逆行驾驶撞车事故的苜蓿叶式立交桥。

目的

本文的重点是开发一种数学方法,以估计此类立交桥出口匝道终端发生逆行驾驶事件的概率。

方法

利用通过现场数据校准的VISSIM交通仿真模型,来估计出口匝道和十字路口在不同交通流量下潜在的逆行驾驶操作数量。在没有现场观测和撞车数据的情况下实施泊松分布模型。

结果

现场数据与仿真输出之间的比较表明,所开发的模型具有可接受的准确度。所提出的模型对进入匝道的左转交通量(LVE)比对出口匝道处的停车车辆(SVE)更为敏感。结果表明,当LVE增加而SVE减少时,潜在的逆行驾驶事件会增加。此外,随着道路使用者对该设施更加熟悉,逆行驾驶事件的概率会降低。

结论

所提出的方法可以减少交通工程师面临的挑战之一,即由于撞车报告中的信息不足而难以识别逆行驾驶撞车事故高发地点。研究结果有助于交通专业人员采取积极措施,确定在高风险信号控制苜蓿叶式立交桥实施安全对策的地点。

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