Fine-Grained Traffic Flow Prediction of Various Vehicle Types via Fusion of Multisource Data and Deep Learning Approaches

计算机科学 流量(计算机网络) 浮动车数据 智能交通系统 传感器融合 深度学习 伤亡人数 数据收集 实时计算 交通拥挤 数据挖掘 运输工程 人工智能 工程类 计算机安全 生物 统计 遗传学 数学
作者
Ping Wang,Wenbang Hao,Yinli Jin
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:22 (11): 6921-6930 被引量:40
标识
DOI:10.1109/tits.2020.2997412
摘要

Both road users and road administrators are keen to know traffic flow of fine-grained vehicle type. Successful prediction on the traffic flow of heavy, medium and small vehicle could contribute to the improvement of travel safety and efficiency. However, the classification on vehicle type is always not accurate enough using in practice. It could cost a lot to identify from the additional video cameras to cover the full-length of large-scale freeway with high-resolution to capture vehicles clearly. In this paper, empirical data are cleaned, normalized, compensated, filled, decoded and filtered with help of the fusion of vehicle detector data, remote microwave sensors data and toll collection data. The traffic flows of fine-grained heavy, medium and small vehicles are successfully reconstructed. Improved deep belief network (DBN) are then proposed to forecast traffic flow of different types of vehicles in 30-, 60- and 120-minutes time interval. Random-selected road segments on a ring way around a city are trained with data accumulated three months and predict data in the next month. According to prediction error analysis, the proposed method performs better in estimation and forecasting, with respect to the existing methods, especially for longer time prediction and heavy vehicle prediction. It would benefit traffic control to prevent freeway congestion escalation, protect the traffic infrastructure via heavy vehicle control, reduce the road risk, prompt quick emergency response and eventually contributes to more applications for intelligent transportation system (ITS).
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