[1] Li Shubin, Kong Xiangke, Li Qingtong, et al. Short-term traffic flow prediction with PSR-XGBoostconsidering chaotic characteristics [J]. Journal of Southeast University (English Edition), 2022, 38 (1): 92-96. [doi:10.3969/j.issn.1003-7985.2022.01.014]

Copy

Short-term traffic flow prediction with PSR-XGBoostconsidering chaotic characteristics()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 1

Page:

92-96

Research Field:

Traffic and Transportation Engineering

Publishing date:

2022-03-20

Info

Title:

Short-term traffic flow prediction with PSR-XGBoostconsidering chaotic characteristics

Author(s):

Li Shubin¹;  2;  Kong Xiangke¹;  Li Qingtong¹;  Lin Zhaofeng¹;  Zhao Zihao³

¹School of Traffic Engineering, Shandong Jianzhu University, Jinan 250101, China
²Department of Traffic Management Engineering, Shandong Police College, Jinan 250014, China
³Beijing Urban Construction Design and De

Keywords:

traffic prediction;  phase space reconstruct;  complex networks;  model optimization

PACS:

U491.1

DOI:

10.3969/j.issn.1003-7985.2022.01.014

Abstract:

To improve the level of active traffic management, a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms. Firstly, the traditional data preprocessing method is improved. The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states. Secondly, one-dimensional data are mapped into a multidimensional data matrix through PSR, and the time series complex network is used to verify the data reconstruction effect. Finally, the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters. The experimental results show that the mean square error, average absolute error, and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%, 1.632%, and 6.278%, respectively, and the required running time is 17.35 s. Compared with mathematical-statistical models and other machine learning models, the PSR-XGBoost model has clear advantages in multiple predictive indicators, proving its feasibility and superiority in short-term traffic flow prediction.

References:

[1] Han C, Song S, Wang C H. A real-time short-term traffic flow adaptive forecasting method based on ARIMA model[J]. Journal of System Simulation, 2004, 16(7): 1530-1532, 1535. DOI:10.3969/j.issn.1004-731X.2004.07.042. (in Chinese)
[2] Yang G F, Xu R, Qin M, et al. Short-term traffic volume forecasting based on ARMA and Kalman filter[J]. Journal of Zhengzhou University(Engineering Science), 2017, 38(2): 36-40. DOI:10.13705/j.issn.1671-6833.2017.02.009. (in Chinese)
[3] Tian Z D. Chaotic characteristic analysis of network traffic time series at different time scales[J]. Chaos Solitons & Fractals, 2020, 130: 109412. DOI: 10.1016/j.chaos.2019.109412.
[4] Jia X C, Chen X M, Gong J L, et al. Multi-step short-term traffic flow prediction based on chaotic theory [J]. Journal of Transport Information and Safety, 2013, 31(6): 27-32. DOI:10.3963/j.issn.1674-4861.2013.06.006. (in Chinese)
[5] Wu Q. Research and application of short-term traffic flow forcasting based on support vector machine regression[D]. Xi’an: Chang’an University, 2016.(in Chinese)
[6] Zhang L Z, Alharbe N R, Luo G C, et al. A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction[J]. Tsinghua Science and Technology, 2018, 23(4): 479-492. DOI: 10.26599/TST.2018.9010045.
[7] Ma X L, Tao Z M, Wang Y H, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J].Transportation Research Part C: Emerging Technologies, 2015, 54: 187-197. DOI: 10.1016/j.trc.2015.03.014.
[8] Zhao J D, Gao Y, Yang Z Z, et al. Truck traffic speed prediction under nonrecurrent congestion: Based on optimized deep learning algorithms and GPS data[J]. IEEE Access, 2019, 7: 9116-9127. DOI: 10.1109/ACCESS.2018.2890414.
[9] Li Y Y. Research on the short-term traffic flow forecasting method of based on phase space reconstruction and SVR[D]. Beijing: Beijing Jiaotong University, 2018.(in Chinese)
[10] Matilla García M, Morales I, Rodríguez J M, et al. Selection of embedding dimension and delay time in phase space reconstruction via symbolic dynamics[J]. Entropy, 2021, 23(2): 221-221. DOI: 10.3390/e23020221.
[11] Gao Z K, Jin N D. Complex network from time series based on phase space reconstruction[J]. Chaos, 2009, 19(3): 033137. DOI: 10.1063/1.3227736.
[12] Chen T Q, Guestrin C. XGBoost: A scalable tree boosting system[C]// The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA, 2016: 785-794. DOI: 10.1145/2939672.2939785.

Memo

Memo:

Biography: Li Shubin(1977—), male, doctor, professor, li_shu_bin@153.com.
Foundation items: The National Natural Science Foundation of China(No.71771019, 71871130, 71971125); the Science and Technology Special Project of Shandong Provincial Public Security Department(No. 37000000015900920210010001, 37000000015900920210012001).
Citation: Li Shubin, Kong Xiangke, Li Qingtong, et al.Short-term traffic flow prediction with PSR-XGBoost considering chaotic characteristics[J].Journal of Southeast University(English Edition), 2022, 38(1):92-96.DOI:10.3969/j.issn.1003-7985.2022.01.014.

Common functions