A congestion prediction method based on trajectory mining algorithm

Abstract

Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been proposed. Almost all these methods are based on traffic flow prediction algorithm. In these methods, historical traffic flow data is used while performing prediction. Obviously, information of sudden accidents like traffic accidents, road damage and bad weather that happened recently may be not contained in historical traffic flow data. But performance of traffic flow prediction algorithms will be affected by these factors. In this situation, performance of traffic congestion prediction method based on traffic flow prediction result will be affected as well. To solve the problem, a new traffic congestion prediction method based on trajectory mining algorithm is proposed in this paper. In this method, traffic controllers can set a threshold for each road according to the current situation of the road. The threshold represents the vehicle number that can be carried by the corresponding road in a short period. Besides, for each road, the proposed method tries to count the number of vehicles that will pass through the specific road at next time step by predicting next location for all the running vehicles based on their trajectories. If the vehicle number of a road surpasses the threshold of this road, it will be predicted as congested road. Otherwise, it will be predicted as non-congested road.

References

[1] Ahmed, M. S. & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. The National Academies of Sciences, Engineering, and Medicine, Washington, DC. vol. 722. https://trid.trb.org/View/148123.

[2] Baher Abdulhai HP, Recker W. Short-term traffic flow prediction using neuro-genetic algorithms Journal of Intelligent Transportation Systems, 2002, 7(1): 3-41

[3] Dougherty MS, Cobbett MR. Short-term inter-urban traffic forecasts using neural networks International Journal of Forecasting, 1997, 13(1): 21-31.

[4] Feng X, Ling X, Zheng H, Chen Z, Xu Y. Adaptive multi-kernel svm with spatial–temporal correlation for short-term traffic flow prediction IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6): 2001-2013.

[5] Habtemichael, F. G. & Cetin, M. (2016). Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transportation Research Part C: Emerging Technologies, 66, 61–78. Advanced Network Traffic Management: From dynamic state estimation to traffic control.

[6] Hou Q, Leng J, Ma G, Liu W, Cheng Y. An adaptive hybrid model for short-term urban traffic flow prediction Physica A: Statistical Mechanics and its Applications, 2019, 527: 121065.

[7] Kamarianakis Y, Prastacos P. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches Transportation Research Record, 2003, 1857(1): 74-84.

[8] Lingras P, Mountford P Monostori L, Váncza J, Ali M. Time delay neural networks designed using genetic algorithms for short term inter-city traffic forecasting Engineering of Intelligent Systems, 2001 Berlin Heidelberg Springer 290-299.

[9] Liu B, Tang X, Cheng J, Shi P. Traffic flow combination forecasting method based on improved lstm and arima International Journal of Embedded Systems, 2020, 12(1): 22-30.

[10] Mathew, W., Raposo, R., & Martins, B. (2012). Predicting future locations with hidden markov models. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp ’12 (pp. 911–918). Association for Computing Machinery.

[11] Mou L, Zhao P, Xie H, Chen Y. T-lstm: A long short-term memory neural network enhanced by temporal information for traffic flow prediction IEEE Access, 2019, 7: 98053-98060.

[12] Shahriari Siroos, Milad Ghasri SAS, Rashidi T. Ensemble of arima: Combining parametric and bootstrapping technique for traffic flow prediction Transportmetrica A: Transport Science, 2020, 16(3): 1552-1573.

[13] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting The journal of machine learning research, 2014, 15(1): 1929-1958

[14] Sun J, Kim J. Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networks Transportation Research Part C: Emerging Technologies, 2021, 128: 103114.

[15] Tian Y, Zhang K, Li J, Lin X, Yang B. Lstm-based traffic flow prediction with missing data Neurocomputing, 2018, 318: 297-305.

[16] Wang, J. & Shi, Q. (2013). Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory. Transportation Research Part C: Emerging Technologies, 27, 219–232. Selected papers from the Seventh Triennial Symposium on Transportation Analysis (TRISTAN VII).

[17] Williams BM, Durvasula PK, Brown DE. Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models Transportation Research Record, 1998, 1644(1): 132-141.

[18] Xia D, Wang B, Li H, Li Y, Zhang Z. A distributed spatial–temporal weighted model on mapreduce for short-term traffic flow forecasting Neurocomputing, 2016, 179: 246-263.

[19] Xiao, Y. & Yin, Y. (2019). Hybrid lstm neural network for short-term traffic flow prediction. Information, 10(3):1–22. https://www.mdpi.com/2078-2489/10/3/105.

[20] Yang D, Li S, Peng Z, Wang P, Wang J, Yang H. MF-CNN: Traffic flow prediction using convolutional neural network and multi-features fusion IEICE TRANSACTIONS on Information and Systems, 2019, 102(8): 1526-1536.

[21] Yu B, Song X, Guan F, Yang Z, Yao B. k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition Journal of Transportation Engineering, 2016, 142(6): 04016018.

[22] Zhang W, Yu Y, Qi Y, Shu F, Wang Y. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning Transportmetrica A: Transport Science, 2019, 15(2): 1688-1711.

[23] Zheng, G., Mukherjee, S., Dong, X. L., & Li, F. (2018). Opentag: Open attribute value extraction from product profiles. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18 (pp. 1049–1058). Association for Computing Machinery.

Funding

National Natural Science Foundation of China(62062054)

Inner mongolia basic scientific research expenses of universities and colleges(JY20220257)

Inner Mongolia Universities’ Youth Science and Technology personnel development project(NJYT24035)

Inner mongolia basic scientific research expenses of universities and colleges(JY20220283)

Natural Science Foundation of Inner Mongolia Autonomous Region(2024MS06021)

RIGHTS & PERMISSIONS

The Author(s)