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City traffic prediction based on real-time traffic information for Intelligent Transport Systems

机译：基于实时交通信息的智能交通系统城市交通预测

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摘要

Intelligent Transportation Systems (ITS) have been considered important technologies to mitigate urban traffic congestion. Accurate traffic prediction is one of the critical steps in the operation of an ITS. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on highway networks. Due to the fundamental difference between the traffic flow pattern on highways and that on city roads, much of the existing models cannot be effectively applied to city traffic prediction. In this paper, we propose two city traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road link. The proposed models are implemented to predict the traffic volume in Cologne in Germany, and the real data are collected through simulations in the traffic simulator SUMO. The results show that both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model. In addition, we found that Model-1 is suitable for short prediction interval that is in the same magnitude as the link travel time, while Model-2 demonstrates superiority when the prediction interval is larger than one minute.
机译：智能交通系统（ITS）被认为是缓解城市交通拥堵的重要技术。准确的流量预测是ITS操作中的关键步骤之一。尽管交通预测技术已经存在多年，但研究工作主要集中在高速公路网络上。由于高速公路上的交通流模式与城市道路上的交通流模式之间存在根本差异，因此许多现有模型无法有效地应用于城市交通预测。在本文中，我们提出了两种使用不同建模方法的城市交通预测模型。模型1基于网络中的交通流传播，而模型2基于相关道路链路上随时间变化的备用流量。所提出的模型用于预测德国科隆的交通量，并通过交通模拟器SUMO中的模拟收集实际数据。结果表明，与现有的Shift模型相比，在最佳情况下，两个建议的模型都将预测误差分别降低了52％和30％。此外，我们发现Model-1适用于与链接行进时间大小相同的短预测间隔，而Model-2在预测间隔大于一分钟时表现出优越性。

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