Abstract
In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models.
Highlights
IntroductionThe high quality and efficient operation of an urban transportation system is an important foundation for the development and construction of modern city, but is the main link to maintaining the daily work and life of the city and its surrounding areas
This paper takes the traffic information collected by remote traffic microwave sensors (RTMS) of all national, provincial, and county roads outside of the Fifth Ring Road in Beijing as the data basis for analysis, taking the section where the detector of Jingkai auxiliary road is located as the target section, and taking a total of 49 detector sections in Daxing District where the detector is located as the alternative section (15 detectors have no data during data extraction)
This paper proposes a fusion deep learning model considering spatial–temporal correlation to solve the problem of urban road traffic flow prediction and improve prediction accuracy
Summary
The high quality and efficient operation of an urban transportation system is an important foundation for the development and construction of modern city, but is the main link to maintaining the daily work and life of the city and its surrounding areas. Traffic flow prediction can be defined as the process of estimating the traffic flow state at a future time [1]. Real-time and accurate traffic flow prediction can improve the operation efficiency of urban roads and provide theoretical support for traffic management decision-making. Accurate predictive information can optimize individual travelers’ travel planning and save on their travel time. An accurate traffic flow prediction is a key issue in the development of intelligent transportation systems (ITS) in the future
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