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Application of dynamic lane grouping and artificial intelligence techniques in predicting the optimum lane groups at isolated signalized intersections

机译：动态车道分组和人工智能技术在隔离信号交叉口预测最佳车道组中的应用

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摘要

Signalized intersection is an important element of any road network. Its operations impact adversely the environment and safety and further affect significantly the performance of the whole road system. A considerable variability in traffic demand is expected at most signalized intersections in urban areas. Most of such intersections nowadays are prone to the phenomenon of tide traffic where different traffic movements at each approach (left, through and right) are fluctuating significantly with time. This phenomenon has a significant role in degrading intersections performance and results in congestion along with excessive emissions of harmful gases. This study was conducted to investigate the effectiveness of applying dynamic lane assignment strategy, which is also known as dynamic lane grouping, to optimize signal timing plans. The concept of Dynamic Lane Grouping (DLG) has been introduced to mitigate such operation problems. MATLAB environment was used to build an optimization model to find the optimal lane groups at all intersection approaches for hypothetical massive traffic demand combinations using an objective function of minimizing intersection delay. A comparison was conducted between the average intersection delay for DLG and Fixed Lane Grouping (FLG) at different demand combinations. It is observed that applying DLG yields a significant reduction in average intersection delay compared to FLG. This study also introduced a plausible quick method to predict the optimum lane group in the field instantaneously using the percentage of turning movements at the approach without conducting massive calculations. On the other hand, interviews were conducted to explore the drivers' response to the information about the existing configuration when disseminated via Variable Message Signs (VMS). The effect of drivers' characteristics, such as age, occupation, driving experience and education level on their response to VMS, was statistically tested using contingency analysis. It was found that the most significant variable that will affect the drivers' understanding of VMS is the level of education. Moreover, the Artificial Neural Networks (ANN) model was developed to predict the optimal lane group combinations for any turning movement combinations with an average accuracy of 92%.
机译：信号交叉口是任何道路网络的重要组成部分。其运营对环境和安全产生不利影响，并进一步严重影响整个道路系统的性能。在城市地区大多数信号交叉口，交通需求会出现很大变化。如今，大多数此类交叉路口都容易出现潮汐交通现象，每种进场（左，右，右）的不同交通流量都随时间而显着波动。这种现象在降低交叉路口的性能方面起着重要作用，并导致拥堵以及有害气体的过度排放。进行这项研究是为了研究应用动态车道分配策略（也称为动态车道分组）来优化信号时序计划的有效性。已引入动态车道分组（DLG）的概念，以减轻此类操作问题。使用MATLAB环境构建了一个优化模型，使用最小化交叉路口延误的目标函数，在所有交叉路口方法中为假设的大量交通需求组合找到最佳车道组。在不同需求组合下，DLG和固定车道分组（FLG）的平均交叉路口延迟之间进行了比较。可以看出，与FLG相比，应用DLG可以显着降低平均交叉延迟。这项研究还介绍了一种可行的快速方法，该方法可使用进场中的转弯运动百分比即时预测现场的最佳车道组，而无需进行大量计算。另一方面，进行了访谈，以探索驾驶员对通过可变消息符号（VMS）传播的现有配置信息的反应。使用权变分析对驾驶员的特征（例如年龄，职业，驾驶经验和受教育程度）对其对VMS的响应的影响进行了统计检验。研究发现，影响驾驶员对VMS理解的最重要变量是教育程度。此外，还开发了人工神经网络（ANN）模型来预测任何转弯运动组合的最佳车道组组合，平均准确度为92％。

著录项

  • 作者

    Assi, Khaled Jamal.;

  • 作者单位

    King Fahd University of Petroleum and Minerals (Saudi Arabia).;

  • 授予单位 King Fahd University of Petroleum and Minerals (Saudi Arabia).;
  • 学科 Civil engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:45

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