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Traffic Level of Service Prediction by Support Vector Machine, Deep Neural Network and Long Short-Term Memory Models | ||
| AUT Journal of Civil Engineering | ||
| مقاله 12، دوره 5، شماره 4، اسفند 2021، صفحه 715-726 اصل مقاله (999.3 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2022.21090.5791 | ||
| نویسندگان | ||
| Arash Rasaizadi1؛ Seyedehsan Seyedabrishami* 1؛ Mohammad Saniee Abadeh2 | ||
| 1School of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| 2School of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده | ||
| Short-term prediction of traffic parameters and informing them to travelers and transportation operators is a useful tool for advanced traveler information systems. Also, as an advanced traffic management system, it helps to make or maintains the balance between travel demand and supply for the near future. This paper predicts the hourly traffic level of service, which has easily understandable information for all users. Data used in this study is related to 5 sections of a critical suburban road in the north of Iran. This data was collected for five years, and due to its high volume, it is considered big data. Long short term memory and deep neural network as two deep learning algorithms and support vector machine as a well-known classifier are trained by the first four years records. Results show that in average long short term memory predictions are more accurate for all sections, which compared to the second precise model, long short term memory predictions are higher between 1 and 14%. Using long short term memory for predicting level of services A and C, support vector machine for predicting level of services B and D and deep neural network for predicting E and F, bring the highest accuracy for each level of service. | ||
| کلیدواژهها | ||
| Big data؛ Short-term prediction؛ Support vector machine؛ Deep neural network؛ Long short-term memory | ||
| مراجع | ||
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[1] B. Yao, C. Chen, Q. Cao, L. Jin, M. Zhang, H. Zhu, B. Yu, Short‐term traffic speed prediction for an urban corridor, Computer‐Aided Civil and Infrastructure Engineering, 32(2) (2017) 154-169.
[2] X. Ma, Z. Tao, Y. Wang, H. Yu, Y. Wang, Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transportation Research Part C: Emerging Technologies, 54 (2015) 187-197.
[3] B. Priambodo, A. Ahmad, Predicting traffic flow based on average speed of neighboring road using multiple regression, in: International Visual Informatics Conference, Springer, 2017, pp. 309-318.
[4] J. Tang, F. Liu, Y. Zou, W. Zhang, Y. Wang, An improved fuzzy neural network for traffic speed prediction considering periodic characteristic, IEEE Transactions on Intelligent Transportation Systems, 18(9) (2017) 2340-2350.
[5] A. Rasaizadi, S.E. Seyedabrishami, Traffic state prediction by machine learning algorithms for short-term and mid-term prediction time horizons, Amirkabir Journal of Civil Engineering, (2021).
[6] A. Rasaizadi, E. Sherafat, S. Seyedabrishami, Short-term prediction of traffic state, statistical approach versus machine learning approach, Scientia Iranica, (2021).
[7] Q. Liu, Y. Cai, H. Jiang, J. Lu, L. Chen, Traffic state prediction using ISOMAP manifold learning, Physica A: Statistical Mechanics and its Applications, 506 (2018) 532-541.
[8] B. Coifman, Freeway detector assessment: aggregate data from remote traffic microwave sensor, Transportation research record, 1917(1) (2005) 149-163.
[9] J. Xu, D. Deng, U. Demiryurek, C. Shahabi, M. Van der Schaar, Mining the situation: Spatiotemporal traffic prediction with big data, IEEE Journal of Selected Topics in Signal Processing, 9(4) (2015) 702-715.
[10] Y. Lv, Y. Duan, W. Kang, Z. Li, F.-Y. Wang, Traffic flow prediction with big data: a deep learning approach, IEEE Transactions on Intelligent Transportation Systems, 16(2) (2014) 865-873.
[11] E.I. Vlahogianni, J.C. Golias, M.G. Karlaftis, Short‐term traffic forecasting: Overview of objectives and methods, Transport Reviews, 24(5) (2004) 533-557.
[12] A. Rasaizadi, A. Ardestani, S. Seyedabrishami, Traffic management via traffic parameters prediction by using machine learning algorithms, International Journal of Human Capital in Urban Management, 6(1) (2021) 57-68.
[13] Q.T. Tran, Cellular network traffic prediction using exponential smoothing methods, Journal of Information and Communication Technology, 18(1) (2020) 1-18.
[14] Z.-d. Tian, S.-j. Li, Y.-h. Wang, X.-d. Wang, Network traffic prediction based on ARIMA with Gaussian process regression compensation, Journal of Beijing University of Posts and Telecommunications, 40(6) (2017) 65-73.
[15] S.V. Kumar, Traffic flow prediction using Kalman filtering technique, Procedia Engineering, 187 (2017) 582-587.
[16] M.G. Karlaftis, E.I. Vlahogianni, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transportation Research Part C: Emerging Technologies, 19(3) (2011) 387-399.
[17] S. Seyedabrishami, A.R. Izadi, H.S. Rayaprolu, R. Moeckel, Car ownership: A joint model for number of cars and fuel types, Transportation Research Procedia, 41 (2019).
[18] A. Rasaizadi, M. Kermanshah, Mode choice and number of non-work stops during the commute: Application of a copula-based joint model, Scientia Iranica, 25(3) (2018) 1039-1047.
[19] X. Pan, W. Zhou, Y. Lu, N. Sun, Prediction of network traffic of smart cities based on DE-BP neural network, IEEE Access, 7 (2019) 55807-55816.
[20] M. Arif, G. Wang, S. Chen, Deep learning with non-parametric regression model for traffic flow prediction, in: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), IEEE, 2018, pp. 681-688.
[21] I. Okutani, Y.J. Stephanedes, Dynamic prediction of traffic volume through Kalman filtering theory, Transportation Research Part B: Methodological, 18(1) (1984) 1-11.
[22] S.V. Kumar, L. Vanajakshi, Short-term traffic flow prediction using seasonal ARIMA model with limited input data, European Transport Research Review, 7(3) (2015) 21.
[23] D. Huang, Z. Deng, L. Zhao, B. Mi, A short-term traffic flow forecasting method based on Markov Chain and Grey Verhulst model, in: 2017 6th Data Driven Control and Learning Systems (DDCLS), IEEE, 2017, pp. 606-610.
[24] N. Yudin, J. Laska, Are we there yet? Traffic flow prediction via Markov Random Fields, Citeseer, 2009.
[25] J. Sun, J. Sun, A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data, Transportation Research Part C: Emerging Technologies, 54 (2015) 176-186.
[26] D.-w. Xu, Y.-d. Wang, L.-m. Jia, Y. Qin, H.-h. Dong, Real-time road traffic state prediction based on ARIMA and Kalman filter, Frontiers of Information Technology & Electronic Engineering, 18(2) (2017) 287-302.
[27] A. Jayan, S.P. Anusha, Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors, Periodica Polytechnica Transportation Engineering, (2017).
[28] H. Yang, Y.-h. Zhang, L. Zhang, T. Xu, Research on Floating Car Speed Short-Time Prediction with Wavelet–ARIMA Under Data, in: Green Intelligent Transportation Systems: Proceedings of the 8th International Conference on Green Intelligent Transportation Systems and Safety, Springer, 2018, pp. 289.
[29] R. More, A. Mugal, S. Rajgure, R.B. Adhao, V.K. Pachghare, Road traffic prediction and congestion control using artificial neural networks, in: 2016 International Conference on Computing, Analytics and Security Trends (CAST), IEEE, 2016, pp. 52-57.
[30] A. Rasaizadi, S. Seyedabrishami, M. Saniee Abadeh, Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process, Journal of Advanced Transportation, 2021 (2021).
[31] Q.-m. Wang, A.-w. Fan, H.-s. Shi, Network traffic prediction based on improved support vector machine, International Journal of System Assurance Engineering and Management, 8(3) (2017) 1976-1980.
[32] B. Sun, W. Cheng, P. Goswami, G. Bai, Short-term traffic forecasting using self-adjusting k-nearest neighbors, IET Intelligent Transport Systems, 12(1) (2017) 41-48.
[33] M. Shuai, K. Xie, W. Pu, G. Song, X. Ma, An Online Approach Based on Locally Weighted Learning for Real Time Traffic Flow Prediction, in: The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008, Citeseer, 2008.
[34] B. Yang, S. Sun, J. Li, X. Lin, Y. Tian, Traffic flow prediction using LSTM with feature enhancement, Neurocomputing, 332 (2019) 320-327.
[35] Y. Wu, H. Tan, L. Qin, B. Ran, Z. Jiang, A hybrid deep learning based traffic flow prediction method and its understanding, Transportation Research Part C: Emerging Technologies, 90 (2018) 166-180.
[36] X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang, Y. Wang, Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction, Sensors, 17(4) (2017) 818.
[37] Y. Tian, K. Zhang, J. Li, X. Lin, B. Yang, LSTM-based traffic flow prediction with missing data, Neurocomputing, 318 (2018) 297-305.
[38] H. Tan, Y. Wu, B. Shen, P.J. Jin, B. Ran, Short-term traffic prediction based on dynamic tensor completion, IEEE Transactions on Intelligent Transportation Systems, 17(8) (2016) 2123-2133.
[39] H. Yao, X. Tang, H. Wei, G. Zheng, Z. Li, Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2019, pp. 5668-5675.
[40] W. Li, J. Wang, R. Fan, Y. Zhang, Q. Guo, C. Siddique, X.J. Ban, Short-term traffic state prediction from latent structures: Accuracy vs. efficiency, Transportation Research Part C: Emerging Technologies, 111 (2020) 72-90.
[41] Y. Liu, Z. Liu, H.L. Vu, C. Lyu, A Spatio-temporal ensemble method for large‐scale traffic state prediction, Computer‐Aided Civil and Infrastructure Engineering, 35(1) (2020) 26-44.
[42] D. Xu, Y. Wang, P. Peng, S. Beilun, Z. Deng, H. Guo, Real-time road traffic state prediction based on kernel-knn, Transportmetrica A: Transport Science, 16(1) (2020) 104-118.
[43] H. Wang, L. Liu, S. Dong, Z. Qian, H. Wei, A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework, Transportmetrica B: Transport Dynamics, 4(3) (2016) 159-186.
[44] X. Luo, D. Li, Y. Yang, S. Zhang, Spatiotemporal traffic flow prediction with KNN and LSTM, Journal of Advanced Transportation, 2019 (2019).
[45] S. Jiang, W. Chen, Z. Li, H. Yu, Short-term demand prediction method for online car-hailing services based on a least squares support vector machine, IEEE Access, 7 (2019) 11882-11891.
[46] N.C. Petersen, F. Rodrigues, F.C. Pereira, Multi-output bus travel time prediction with convolutional LSTM neural network, Expert Systems with Applications, 120 (2019) 426-435.
[47] Y. Liu, Z. Liu, R. Jia, DeepPF: A deep learning based architecture for metro passenger flow prediction, Transportation Research Part C: Emerging Technologies, 101 (2019) 18-34.
[48] B. Yu, Z. Guo, S. Asian, H. Wang, G. Chen, Flight delay prediction for commercial air transport: A deep learning approach, Transportation Research Part E: Logistics and Transportation Review, 125 (2019) 203-221.
[49] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature, 521(7553) (2015) 436-444.
[50] J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, 61 (2015) 85-117.
[51] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation, 9(8) (1997) 1735-1780.
[52] A. Rasaizadi, E. Sherafat, S. Seyedabrishami, Short-term prediction of traffic state, statistical approach versus machine learning approach.
[53] X. Wang, K. An, L. Tang, X. Chen, Short term prediction of freeway exiting volume based on SVM and KNN, International Journal of Transportation Science and Technology, 4(3) (2015) 337-352.
[54] S. Han, C. Qubo, H. Meng, Parameter selection in SVM with RBF kernel function, in: World Automation Congress 2012, IEEE, 2012, pp. 1-4.
[55] A. Rasaizadi, I. Farzin, F. Hafizi, Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips, Physica A: Statistical Mechanics and its Applications, 586 (2022) 126492.
[56] C. Antoniou, H.N. Koutsopoulos, G. Yannis, Dynamic data-driven local traffic state estimation and prediction, Transportation Research Part C: Emerging Technologies, 34 (2013) 89-107.
[57] M. Zahid, Y. Chen, A. Jamal, M.Q. Memon, Short term traffic state prediction via hyperparameter optimization based classifiers, Sensors, 20(3) (2020) 685.
[58] J. Zhu, C. Huang, M. Yang, G.P.C. Fung, Context-based prediction for road traffic state using trajectory pattern mining and recurrent convolutional neural networks, Information Sciences, 473 (2019) 190-201.
[59] R. Toncharoen, M. Piantanakulchai, Traffic state prediction using convolutional neural network, in: 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2018, pp. 1-6. | ||
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