Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

Changxi Ma; Limin Tan; Xuecai Xu

doi:10.7307/ptt.v32i6.3360

Changxi Ma Lanzhou Jiaotong University, School of Traffic and Transportation Engineering
Limin Tan Lanzhou Jiaotong University, School of Traffic and Transportation Engineering
Xuecai Xu Huazhong University of Science and Technology, School of Civil and Hydraulic Engineering

DOI: https://doi.org/10.7307/ptt.v32i6.3360

Keywords: short-term traffic flow prediction, Genetic Artificial Neural Network, Exponential Smoothing, combined model

Abstract

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

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Changxi Ma, Lanzhou Jiaotong University, School of Traffic and Transportation Engineering

Ph.D., Professor

Limin Tan, Lanzhou Jiaotong University, School of Traffic and Transportation Engineering

M.Sc. Student

Xuecai Xu, Huazhong University of Science and Technology, School of Civil and Hydraulic Engineering

Ph.D., Assistant Professor

References

Cheng R, Ge H, Wang J. An extended continuum model accounting for the driver's timid and aggressive attributions. Physics Letters A. 2017;381(15): 1302-1312. Available from: doi:10.1016/j.physleta.2017.02.018 [Accessed 19 June 2020].

Cheng R, Wang Y. An extended lattice hydrodynamic model considering the delayed feedback control on a curved road. Physica A – Statistical Mechanics & Its Applications. 2019;513: 510-517. Available from: doi:10.1016/j.physa.2018.09.014 [Accessed 19 June 2020].

Wu W, Liu R, Jin W. Designing robust schedule coordination scheme for transit networks with safety control margins. Transportation Research Part B – Methodological. 2016;93: 495-519. Available from: doi:10.1016/j.trb.2016.07.009 [Accessed 19 June 2020].

Ma C, Hao W, Wang A, Zhao H. Developing a coordinated signal control system for urban ring road under the vehicle-infrastructure connected environment. IEEE Access. 2018;6: 52471-52478. Available from: doi:10.1109/access.2018.2869890 [Accessed 19 June 2020].

Zou Y, Ash J, Park B, Lord D, Wu L. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics. 2018;45(9): 1652-1669. Available from: doi:10.1080/02664763.2017.1389863 [Accessed 19 June 2020].

Ma C, He R. Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Computing and Applications. 2019;31(7): 2073-2083. Available from: doi:10.1007/s00521-015-1931-y [Accessed 19 June 2020].

Tang J, Liang J, Zhang S, Huang H, Liu F. Inferring driving trajectories based on probabilistic model from large scale taxi GPS data. Physica A – Statistical Mechanics and Its Applications. 2018;506: 566-577. Available from: doi:10.1016/j.physa.2018.04.073 [Accessed 19 June 2020].

Wu W, Liu R, Jin W. Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour. Transportation Research Part B – Methodological. 2017;104: 175-197. Available from: doi:10.1016/j.trb.2017.06.019 [Accessed 19 June 2020].

Hao W, Ma C, Moghimi B, Fan Y. Robust optimization of signal control parameters for unsaturated intersection based on tabu search-artificial bee colony algorithm. IEEE Access. 2018;6: 32015-32022. Available from: doi:10.1109/access.2018.2845673 [Accessed 19 June 2020].

Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B. Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowledge Based Systems. 2019;172: 1-14. Available from: doi:10.1016/j.knosys.2019.01.015 [Accessed 19 June 2020].

Tang J, Liang J, Han C, Li Z, Huang H. Crash injury severity analysis using a two-layer Stacking framework. Accident Analysis & Prevention. 2019;122: 226-238. Available from: doi:10.1016/j.aap.2018.10.016 [Accessed 19 June 2020].

Ma C, Yang D, Zhou J, Feng Z, Yuan Q. Risk riding behaviors of urban e-bikes: A literature review. International Journal of Environmental Research and Public Health. 2019;16(13): 2308. Available from: doi:10.3390/ijerph16132308 [Accessed 19 June 2020].

Zhang R, Ye X, Wang K, Li D, Zhu J. Development of commute mode choice model by integrating actively and passively collected travel data. Sustainability. 2019;11(10): 2730. Available from: doi:10.3390/su11102730 [Accessed 19 June 2020].

Wu W, Liu R, Jin W, Ma C. Stochastic bus schedule coordination considering demand assignment and rerouting of passengers. Transportation Research Part B – Methodological. 2019;121: 275-303. Available from: doi:10.1016/j.trb.2019.01.010 [Accessed 19 June 2020].

Stephanedes YJ, Michalopoulos PG, Plum R. Improved Estimation of Traffic Flow for Real-Time Control (Discussion and Closure). Transportation Research Record. 1981;(795): 28-39.

Clark SD. Traffic prediction using multivariate nonparametric regression. Journal of Transportation Engineering – ASCE. 2003;129(2): 161-168. Available from: doi:10.1061/(ASCE)0733-947X(2003)129:2(161) [Accessed 19 June 2020].

Turochy RE, Pierce BD. Relating Short-Term Traffic Forecasting to Current System State Using Nonparametric Regression. Proceedings of the 7th International Conference on Intelligent Transportation Systems, 3-6 Oct 2004, Washington, USA. New York, USA: IEEE; 2004. p. 239-244.

Hamed MM, Almasaeid HR, Said ZM. Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering – ASCE. 1995;121(3): 249-254. Available from: doi:10.1061/(ASCE)0733-947X(1995)121:3(249) [Accessed 19 June 2020].

Kirby HR, Watson SM, Dougherty M. Should we use neural networks or statistical models for short-term motorway traffic forecasting?. International Journal of Forecasting. 1997;13(1): 43-50. Available from: doi:10.1016/S0169-2070(96)00699-1 [Accessed 19 June 2020].

Whittaker J, Garside S, Lindveld K. Tracking and predicting a network traffic process. International Journal of Forecasting. 1997;13(1): 51-61. Available from: doi:10.1016/S0169-2070(96)00700-5 [Accessed 19 June 2020].

Ahmed MS, Cook AR. Analysis of Freeway Traffic Time-series Data by Using Box-Jenkins Techniques. Transportation Research Record. 1979;(722).

Davis GA, Nihan NL. Using time-series designs to estimate changes in freeway level of service, despite missing data. Transportation Research Part A: General. 1984;18(5-6): 431-438. Available from: doi:10.1016/0191-2607(84)90018-9 [Accessed 19 June 2020].

Levin M, Tsao Y. On Forecasting Freeway Occupancies and Volumes (Abridgment). Transportation Research Record. 1980;(773).

Der Voort MC, Dougherty M, Watson SM. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C – Emerging Technologies. 1996;4(5): 307-318. Available from: doi:10.1016/S0968-090X(97)82903-8 [Accessed 19 June 2020].

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. Available from: doi:10.1016/j.physa.2019.121065 [Accessed 19 June 2020].

Okutani I, Stephanedes YJ. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B – Methodological. 1984;18(1): 1-11. Available from: doi:10.1016/0191-2615(84)90002-X [Accessed 19 June 2020].

Vythoulkas P. Alternative Approaches to Short Term Traffic Forecasting for Use in Driver Information Systems. Transportation and Traffic Theory. 1993;12: 485-506.

Zhou T, Jiang D, Lin Z, Han G, Xu X, Qin J. Hybrid dual Kalman filtering model for short-term traffic flow forecasting. IET Intelligent Transport Systems. 2019;13(6): 1023-1032. Available from: doi:10.1049/iet-its.2018.5385 [Accessed 19 June 2020].

Ye X, Wang K, Zou Y, Lord D. A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data. PLoS One. 2018;13(5): e0197338. Available from: doi:10.1371/journal.pone.0197338 [Accessed 19 June 2020].

Chang H, Yoon B. High-speed data-driven methodology for real-time traffic flow predictions: Practical applications of ITS. Journal of Advanced Transportation. 2018;2018: 1-11. Available from: doi:10.1155/2018/5728042 [Accessed 19 June 2020].

Hong W. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing. 2011;74(12): 2096-2107. Available from: doi:10.1016/j.neucom.2010.12.032 [Accessed 19 June 2020].

Duan M. Short-Time Prediction of Traffic Flow Based on PSO Optimized SVM. In: Huaiyin Institute of Technology and Jiangsu Key Laboratory of Advanced Manufacturing Technology. Proceedings of 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 25-26 Jan 2018, Xiamen, China. New York, USA: IEEE; 2018. p. 41-45.

Cheng A, Jiang X, Li Y, Zhang C, Zhu H. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A – Statistical Mechanics and Its Applications. 2017;466: 422-434. Available from: doi:10.1016/j.physa.2016.09.041 [Accessed 19 June 2020].

Tang J, Chen X, Hu Z, Zong F, Han C, Li L. Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A – Statistical Mechanics and Its Applications. 2019;534: 120642. Available from: doi:10.1016/j.physa.2019.03.007 [Accessed 19 June 2020].

Clark SD, Dougherty MS, Kirby HR. The Use of Neural Networks and Time Series Models for Short Term Traffic Forecasting: A Comparative Study. Proceedings of PTRC 21st Summer Annual Meeting, Seminar D, UMIST, Sep 1993, London, England. London, England: PTRC; 1993.

Smith BL, Demetsky MJ. Short-Term Traffic Flow Prediction: Neural Network Approach. Transportation Research Record. 1994;(1453).

Roos J, Gavin G, Bonnevay S. A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data. Transportation Research Procedia. 2017;26: 53-61. Available from: doi:10.1016/j.trpro.2017.07.008 [Accessed 19 June 2020].

Xu L, Du X, Wang B. Short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm. International Journal of Pattern Recognition and Artificial Intelligence. 2018;32(12): 1850041. Available from: 10.1142/S0218001418500416 [Accessed 19 June 2020].

Duan Z, Yang Y, Zhang K, Ni Y, Bajgain S. Improved deep hybrid networks for urban traffic flow prediction using trajectory data. IEEE Access. 2018;6: 31820-31827. Available from: doi:10.1109/access.2018.2845863 [Accessed 19 June 2020].

Nejadettehad A, Mahini H, Bahrak B. Short-term demand forecasting for online car-hailing services using Recurrent Neural Networks. arXiv: Learning. 2019. Available from: https://arxiv.org/abs/1901.10821v1 [Accessed 19 June 2020].

Xiao Y, Yang Y. Hybrid LSTM neural network for short-term traffic flow prediction. Information-an International Interdisciplinary Journal. 2019;10(3): 105. Available from: doi:10.3390/info10030105 [Accessed 19 June 2020].

Li C, Anavatti SG, Ray T. Short-term traffic flow prediction using different techniques. Proceedings of IECON 2011-37th Annual Conference of the IEEE Industrial Electronics Society, 7-10 Nov 2011, Melbourne, Australia. New York, USA: IEEE; 2011. p. 2423-2428.

Li J, Gao Z, Wang Z, Zhang J. Short-Term Traffic Flow Forecasting Based on Exponential Smoothing and Markov Chains. Computer Systems & Applications. 2013;22(12): 132-135.

Moretti F, Pizzuti S, Panzieri S, Annunziato M. Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing. 2015;167: 3-7. Available from: doi:10.1016/j.neucom.2014.08.100 [Accessed 19 June 2020].

Li S, Liu L, Zhai M. Prediction for Short-Term Traffic Flow Based on Modified PSO Optimized BP Neural Network. Systems Engineering – Theory & Practice. 2012;32(9): 2045-2049.

Jiao B, Ye M. Determination of Hidden Unit Number in a BP Neural Network. Journal of Shanghai Dianji University. 2013;16(03): 113-116+124. Chinese

Leng Z, Gao J, Zhang B, Liu X, Ma Z. Short-Term Traffic Flow Forecasting Model of Optimized BP Neural Network Based on Genetic Algorithm. Proceedings of the 32nd Chinese Control Conference, 26-28 July 2013, Xi'an, China. New York, USA: IEEE; 2013. p. 8125-8129.

Niu H, Tian X, Zhou X. Demand-driven train synchronization for high-speed rail lines. IEEE Transactions on Intelligent Transportation Systems. 2015;16(5): 2642-2652. Available from: doi:10.1109/TITS.2015.2415513 [Accessed 19 June 2020].

Ma C, He R, Zhang W. Path optimization of taxi carpooling. PLoS One. 2018;13(8): e0203221. Available from: doi:10.1371/journal.pone.0203221 [Accessed 19 June 2020].

Ma C, Hao W, Pan F, Xiang W. Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm. PLoS One. 2018;13(6): e0198931. Available from: doi:10.1371/journal.pone.0198931 [Accessed 19 June 2020].

Ma C, Hao W, He R, Jia X, Pan F, Fan J, Xiong R. Distribution path robust optimization of electric vehicle with multiple distribution centers. PLoS One. 2018;13(3): e0193789. Available from: doi:10.1371/journal.pone.0193789 [Accessed 19 June 2020].

Ma C. Network optimization design of Hazmat based on multi-objective genetic algorithm under the uncertain environment. International Journal of Bio-Inspired Computation. 2018;12(4): 236-244. Available from: doi:10.1504/IJBIC.2018.10017837 [Accessed 19 June 2020].

Ke J, Zheng H, Yang H, Chen X. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C – Emerging Technologies. 2017;85: 591-608. Available from: doi:10.1016/j.trc.2017.10.016 [Accessed 19 June 2020].

Wu Y, Tan H, Qin L, Ran B, Jiang Z. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C – Emerging Technologies. 2018;90: 166-180. Available from: doi:10.1016/j.trc.2018.03.001 [Accessed 19 June 2020].

Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B. Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowledge Based Systems. 2019;172: 1-14. Available from: doi:10.1016/j.knosys.2019.01.015 [Accessed 19 June 2020].