带倒计时计时器的信号交叉口排车过程中车辆跟随行为建模

IF 3.3 3区 工程技术 Q2 TRANSPORTATION
Bijul Raveendran , Tom V. Mathew , Nagendra R. Velaga
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引用次数: 0

摘要

倒计时计时器对驾驶员的行为影响很大,在许多国家的信号灯交叉路口被广泛使用。文献显示,在有定时器的情况下,驾驶员行为在时间和空间上可能因车辆类别而异。因此,本文首先根据加速度的变化,分析了定时器在异构交通条件下对驾驶员行为的时间、空间和车辆类别的影响。其次,结合交通的异质性和定时器的影响,开发了一个长短期记忆模型来模拟车辆排放。利用印度喀拉拉邦三个信号灯路口的现场轨迹数据,将该模型的性能与智能驾驶模型的性能进行了比较。模型准确预测了跟车行为,混合间距误差为 0.24 至 0.35,平均间距平方误差为 1.23。其结果优于或与近期数据驱动的汽车跟随模型的结果相当,并明显优于 IDM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling car-following behavior during queue discharge at signalized intersections with countdown timer

Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM.

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来源期刊
CiteScore
6.40
自引率
14.30%
发文量
79
审稿时长
>12 weeks
期刊介绍: Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research. The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.
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