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Analysis and modeling time headway distributions under heavy traffic flow conditions in the urban highways: case of Isfahan

机译:城市高速公路繁忙交通条件下的时空分布分析与建模:以伊斯法罕为例

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摘要

The time headway of vehicles is an important microscopic traffic flow parameter which affects the safety and capacity of highway facilities such as freeways and multi-lane highways. The present paper intends to provide a report on the results of a study aimed at investigating the effect of the lane position on time headway distributions within the high levels of traffic flow. The main issue of this study is to assess the driver's behavior at different highway lanes based on a headway distribution analysis. The study was conducted in the city of Isfahan, Iran. Shahid Kharrazi six-lane highway was selected for collecting the field headway data. The under-study lanes consisted of passing and middle lanes. The appropriate models of headway distributions were selected using a methodology based on Chi-Square test for each lane. Using the selected models, the headway distribution diagrams were predicted for high levels of traffic flow in both the passing and middle lanes and the relationship between statistical criteria of the models and the driver's behaviors were analyzed. The results certify that the appropriate model for the passing lane is different than the one for the middle lane. This is because of a different behavioral operation of drivers which is affected by specific conditions of each lane. Through car-following conditions in the passing lane, a large number of drivers adopt unsafe headways. This shows high risk-ability of driver population which led to considerably differences in capacities and statistical distribution models of two lanes.
机译:车辆的时距是一个重要的微观交通流量参数,它会影响高速公路设施(如高速公路和多车道高速公路)的安全性和通行能力。本文旨在提供一份有关研究结果的报告,该研究旨在调查车道位置对高交通流量水平下时间时距分布的影响。这项研究的主要目的是基于车头时距分布分析来评估驾驶员在不同高速公路车道上的行为。该研究在伊朗伊斯法罕市进行。选择了Shahid Kharrazi六车道公路来收集现场车头数据。学习不足的车道包括通过车道和中间车道。使用基于每个车道的卡方检验的方法,选择合适的车距分布模型。使用选定的模型,可以预测行车道分布图和过道和中间车道的高流量,并分析模型的统计标准与驾驶员行为之间的关系。结果证明,通过车道的合适模型与中间车道的模型不同。这是因为驾驶员的不同行为操作受到每个车道特定条件的影响。在通过车道的情况下,许多驾驶员采用不安全的车道。这表明驾驶员群体具有很高的风险承受能力,这导致两条车道的能力和统计分布模型存在很大差异。

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