ABSTRACT Until recently, urban cities have faced an increasing demand for an efficient system able to help drivers to discover the congested roads and avoid the long queues. In this paper, an Intelligent Traffic Congestion Prediction System (ITCPS) was developed to predict traffic congestion states in roads. The system embeds a Neural Network architecture able to handle the variation of traffic changes. It takes into account various traffic patterns in urban regions as well as highways during workdays and free-days. The developed system provides drivers with the fastest path and the estimated travel time to reach their destination. The performance of the developed system was tested using a big and real-world Global Positioning System (GPS) database gathered from vehicles circulating in Sfax city urban areas, Tunisia as well as the highways linking Sfax and other Tunisian cities. The results of congestion and travel time prediction provided by our system show promise when compared to other non-parametric techniques. Moreover, our model performs well even in cross-regions whose data were not used during training phase.

中文翻译：

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异构时空模式下出行建模的神经拥堵预测系统

摘要直到最近，城市都面临着对能够帮助司机发现拥挤的道路并避免排长队的高效系统的日益增长的需求。在本文中，开发了一种智能交通拥堵预测系统（ITCPS）来预测道路交通拥堵状态。该系统嵌入了一个能够处理流量变化变化的神经网络架构。它考虑了城市地区的各种交通模式以及工作日和自由日期间的高速公路。开发的系统为司机提供最快的路径和到达目的地的估计旅行时间。使用从在斯法克斯市市区行驶的车辆收集的大型真实全球定位系统 (GPS) 数据库对开发系统的性能进行了测试，突尼斯以及连接斯法克斯和其他突尼斯城市的高速公路。与其他非参数技术相比，我们系统提供的拥堵和旅行时间预测结果显示出前景。此外，即使在训练阶段未使用数据的跨区域中，我们的模型也表现良好。