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Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation

机译：优化的CapsNet用于在城市群运输中使用移动传感器数据预测交通拥堵速度

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摘要

Urban swarming transportation (UST) is a type of road transportation where multiple types of vehicles such as cars, buses, trucks, motorcycles, and bicycles, as well as pedestrians are allowed and mixed together on the roads. Predicting the traffic jam speed under UST is very different and difficult from the single road network traffic prediction which has been commonly studied in the intelligent traffic system (ITS) research. In this research, the road network wide (RNW) traffic prediction which predicts traffic jam speeds of multiple roads at once by utilizing citizens’ mobile GPS sensor records is proposed to better predict traffic jam under UST. In order to conduct the RNW traffic prediction, a specific data preprocessing is needed to convert traffic data into an image representing spatial-temporal relationships among RNW. In addition, a revised capsule network (CapsNet), named OCapsNet, which utilizes nonlinearity functions in the first two convolution layers and the modified dynamic routing to optimize the performance of CapsNet, is proposed. The experiments were conducted using real-world urban road traffic data of Jakarta to evaluate the performance. The results show that OCapsNet has better performance than Convolution Neural Network (CNN) and original CapsNet with better accuracy and precision.
机译：城市蜂群运输（UST）是一种道路运输，允许多种类型的车辆（例如汽车，公共汽车，卡车，摩托车，自行车以及行人）在道路上混合在一起。与在智能交通系统（ITS）研究中通常研究的单路网络交通预测相比，在UST下预测交通拥堵速度非常不同且困难。在这项研究中，提出了通过利用市民的移动GPS传感器记录来一次预测多条道路的拥堵速度的全路网（RNW）交通预测，以更好地预测UST下的拥堵状况。为了进行RNW流量预测，需要进行特定的数据预处理以将流量数据转换为表示RNW之间的时空关系的图像。此外，提出了一种经过修改的胶囊网络（CapsNet），名为OCapsNet，该胶囊网络在前两个卷积层中利用非线性函数，并使用改进的动态路由来优化CapsNet的性能。使用雅加达的现实世界城市道路交通数据进行了实验，以评估性能。结果表明，OCapsNet具有比卷积神经网络（CNN）和原始CapsNet更好的性能，并且具有更高的准确性和精度。

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