Prediction of High-speed Traffic Flow in Baoan District in Shenzhen


  • Yanqing Lu



ARIMA model; highway; traffic flow.


Nowadays, with the increasing number of cars, high-speed congestion has become a common phenomenon. This paper studies the traffic flow prediction of an expressway in Shenzhen, aiming to avoid congestion at the expressway during holidays and carry out effective management. The author uses the ARIMA model for research, with data selection of vehicle flow at expressway intersections from December 2002 to June 2023. Firstly, the ADF test is used to check the feasibility of the data. The ACF and PACF are used to determine the values of p and q in the ARIMA model. What is more, the optimal model is selected by comparing the BIC values of the models constructed with different p and q values. Finally, model establishment and analysis are carried out. It is found that in the 14 years after the selected data, the traffic flow is still on the rise, and it is necessary to pay attention to the traffic flow management of this high-speed section.


Download data is not yet available.


Sun Xiaojun, Cao Luling, Pan Hongmei, et al. A new model for predicting freeway traffic flow. Computing technology and automation, 2016, 35(2): 4.

Zhang Chen, Du Junping. Research on the forecasting method of freeway traffic flow. Journal of Beijing Technology and Business University: Natural Science Edition, 2001, 19(4): 4.

He Jiuran, Si Bingfeng. Application of ARIMA-RBF model in urban rail transit passenger flow prediction. Shandong science, 2013, 26(3): 7.

Rui Shaoquan, Kuang Anle. Monthly highway traffic ARIMA forecast model. Journal of Changan University: Natural Science Edition, 2010, 30(4): 5.

Tang Yan, Wang Hongbo, Wang Wanxin. Traffic flow prediction at intersection based on Gaussian radial basis function neural network. Agricultural equipment and vehicle engineering, 2006, 3: 3.

Li Zezheng, Xu Jiangrong, Zhai Guoqing. Highway traffic noise prediction Model based on equivalent vehicle flow. Engineering Construction and Design, 2008, 9: 4.

Wang Chao, Sun Weihua, He Yuanlie. Application of grey prediction model in expressway traffic flow prediction. Journal of Guangdong University of Technology, 2012, 29(1): 3.

Zhang Bomin. Study on short-term forecast of passenger flow of Shanghai-Nanjing intercity railway. Chinese railway, 2014, 9: 6.

Sun Min. Traffic flow analysis and prediction of Chongzun expressway based on ARIMA model. Shandong Communications Technology, 2014, 3: 2.

Zou Aijuan, You Zilin, Wu Dan. Short-term forecast of expressway traffic volume based on ARIMA model. Defense transportation engineering and technology, 2015, 13(6): 4.




How to Cite

Lu, Y. (2024). Prediction of High-speed Traffic Flow in Baoan District in Shenzhen. Highlights in Science, Engineering and Technology, 105, 50-55.