Short-term traffic flow prediction based on vehicle trip chain features

ABSTRACT

Short-term traffic flow prediction can improve the efficiency of transportation operations. Historical data-driven prediction methods have been proved to perform well. However, saturated or oversaturated traffic operations cannot be accurately predicted based only on detector data from a single intersection. This study proposes a short-term traffic prediction method based on vehicle trip chain features. First, the video data is pre-processed and quality assessed. Then, vehicle trip chain features are mined to correlate upstream and downstream intersections.Convolutional neural networks and long-short-term-memory model are built next. The model is launched to train the predictor and output the traffic flow for all turns at each approach to the intersection. After cases we demonstrate that the prediction accuracy of CNNs-LSTM is usually better than other methods, especially during oversaturation. In addition, we demonstrate that vehicle trip chain features can improve prediction accuracy and shorten the time consumed by the model.

KEYWORDS:

• Short-term traffic flow prediction
• video data
• saturated or oversaturated
• vehicle trip chain
• trip chain mining
• CNNS-LSTM

Acknowledgments

This study was supported by the High-tec SMEs Innovation Capacity Improvement Project of the Shandong Province (No. 2022TSGC2279), and the School-City Integration Development Plan Project of Zhangdian District (No. 2021PT0004). The authors would like to acknowledge the financial support for this study provided by the National Natural Science Foundation of China (No. 52302437), and the Doctoral Scientific Research Start-up Foundation of Shandong University of Technology (No. 422049).

Disclosure statement

No potential conflict of interest was reported by the author(s).

CRediT authorship contribution statement

Xiaoqing Wang: Methodology, Visualization, Writing – original draft. Feng Sun: Conceptualization, Data curation, Funding acquisition. Xiaolong Ma: Resources. Fangtong Jiao : Formal analysis, Funding acquisition, Writing – review and editing. Benxing Liu: Investigation. Pengsheng Zhao: Investigation.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [52302437]; Doctoral Scientific Research Start-up Foundation of Shandong University of Technology [422049]; High-tec SMEs Innovation Capacity Improvement Project of the Shandong Province [2022TSGC2279]; School-City Integration Development Plan Project of Zhangdian District [2021PT0004].