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Short-Term Traffic Flow Prediction for Electronic Toll Collection and Manual Toll Collection Charging System Based on Long Short-Term Memory Model
Technical Paper
2020-01-5197
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Intelligent Transportation System (ITS) plays an important role in smart city, and accurate short-term traffic flow prediction is a significant part. At present, China’s ITS has developed rapidly, and advanced intelligent transportation systems have been built in major cities, such as Shanghai, Shenzhen and so on. With the promotion of mixed Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) charging systems, the features of the traffic flow data have become richer. Traffic data recorded some information for the vehicles entering and exiting highway toll station including time, location, type, mileage, then we can use historical OD data to do traffic flow prediction, predict the corresponding future exit station traffic flow. Furthermore, due to the deep learning network’s ability to model deep complex non-linear relationship in data, researchers have paid more attention to predict traffic flow using deep learning models in recent years. In this paper, we explore a short-term traffic flow prediction method using data from the Xinqiao toll station in Shanghai, China in August 2019 based on LSTM network. The main work includes: At first, 11 entry stations are selected of the Xinqiao toll station, which contribute over 90% traffic data. Secondly, we divided entry stations into ETC and MTC lanes. Thirdly, we use OD data to forecast the traffic flow of the Xinqiao station for ETC and MTC by using LSTM algorithm. Finally, we evaluate this model’s performance, including the weekday, the weekend. Results indicate that the R2 value of ETC can achieve 0.98, and the R2 value of MTC can achieve 0.94.
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Citation
Zhu, C., Li, Z., Wu, B., Li, B. et al., "Short-Term Traffic Flow Prediction for Electronic Toll Collection and Manual Toll Collection Charging System Based on Long Short-Term Memory Model," SAE Technical Paper 2020-01-5197, 2020, https://doi.org/10.4271/2020-01-5197.Data Sets - Support Documents
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