پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 448 |
| تعداد مقالات | 5,737 |
| تعداد مشاهده مقاله | 8,058,164 |
| تعداد دریافت فایل اصل مقاله | 6,555,491 |
پیشبینی وضعیت ترافیک با الگوریتمهای یادگیری ماشین برای افقهای زمانی کوتاه مدت و میان مدت | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 4، تیر 1401، صفحه 1503-1520 اصل مقاله (1.7 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.19650.7219 | ||
| نویسندگان | ||
| آرش رساءایزدی1؛ سید احسان ابریشمی* 2 | ||
| 1برنامه ریزی حمل و نقل، دانشکده مهندسی عمران، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2استادیار، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس | ||
| چکیده | ||
| پیشبینی متغیرهای ترافیکی و اطلاعرسانی آن به مسافرین و گردانندگان شبکه حملونقل یکی از راهکارهای مدیریت تقاضای سفر است. با اطلاعرسانی وضعیت آینده ترافیک از طریق سیستمهای حملونقل هوشمند، آمادگی بیشتری جهت اجتناب از وقوع تراکم ترافیک به وجود میآید. در این مطالعه به منظور پیشبینی وضعیت ترافیک ساعتی، شامل سه وضعیت روان، نیمهسنگین و سنگین، در جاده برونشهری کرج به چالوس در شمال ایران، سه مدل یادگیری ماشین، شامل ماشین بردار پشتیبان، جنگل تصادفی و حافظه طولانی کوتاه مدت به دو صورت کوتاه مدت و میان مدت آموزش داده شدهاند. متغیرهای پیشبینی کننده در مدلهای میان مدت اطلاعات تقویمی، آب و هوا و محدودیتهای ترافیکی هستند در صورتی که در مدلهای کوتاه مدت علاوه بر متغیرهای نام برده، وضعیت ترافیک مشاهده شده در سه تا هشت ساعت گذشته نیز استفاده شده است و این مدلها تنها قادر به پیشبینی وضعیت ترافیک یک و دو ساعت آینده هستند. نتایج نشان میدهد مدل حافظه طولانی کوتاه مدت با دقتی معادل با 90/11 درصد دقیقترین مدل پیشبینی کننده وضعیت ترافیک با افق کوتاه مدت است. این مدل برای افق بلند مدت نیز متغیر وضعیت ترافیک را با 82/07 درصد دقت، دقیقتر از دو مدل دیگر پیشبینی کرده است و بیشترین مقادیر شاخص F (F1) برای پیشبینی سه وضعیت ترافیک سبک، نیمهسنگین و سنگین را به همراه داشته که به ترتیب برابر با 0/86، 0/93 و 0/81 به دست آمدهاند. همچنین متغیرهای ساعت و تعطیلی همان روز و نوع تعطیلی و متغیرهای مشاهدات سه تا هشت ساعت پیش وضعیت ترافیک به ترتیب بیشترین تأثیر را بر افزایش دقت مدلهای میان مدت و کوتاه مدت دارند. | ||
| کلیدواژهها | ||
| پیشبینی وضعیت ترافیک؛ ماشین بردار پشتیبان؛ جنگل تصادفی؛ حافظه طولانی کوتاهمدت؛ سیستمهای حملونقل هوشمند | ||
| موضوعات | ||
| یادگیری ماشین؛ ترافیک شهری و بیرون شهری؛ راه و ترابری و حمل و نقل - ترابری هوشمند؛ راه و ترابری و حمل و نقل - مدل سازی؛ مدیریت ترافیک | ||
| عنوان مقاله [English] | ||
| Traffic state prediction with machine learning algorithms for short-term and mid-term prediction time horizons | ||
| نویسندگان [English] | ||
| Arash Rasaizadi1؛ Seyed Ehsan Seyedabrishami2 | ||
| 1Transportation Planning, Civil engineering department, Tarbiat Modares University, Tehran, Iran | ||
| 2Assistant Professor at the Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Predicting traffic variables and informing the passengers and the transportation network operators is one way to manage the travel demand. By informing the future state of traffic through intelligent transportation systems, there is more readiness to avoid congestion. In this study, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and long short-term memory (LSTM), were used to predict the hourly traffic state, consist of light, semi-heavy and heavy states, for Karaj to Chaloos rural road in the north of Iran. Predictor variables of mid-term models are calendar information, weather, and road blockage policies. In contrast, in short-term models, in addition to the mentioned variables, the observed traffic states in the past three to eight hours have been used, and these models can only predict the future of one and two hours. The results show that short-term LSTM is the most accurate traffic state predictive model, with an accuracy equal to 90.11%. Among the mid-term models, the LSTM model has predicted traffic state more accurately than SVM and RF, and its accuracy is equal to 82.07%. Also, LSTM has the highest values of f1 measure to predict light, semi-heavy, and heavy, which are equivalent to 0.86, 0.93, and 0.81, respectively. Also, the hour, holiday, and type of holiday variables and traffic state observed in 3 to 8 hours later variables have the greatest effect on increasing the accuracy of mid-term and short-term models, respectively. | ||
| کلیدواژهها [English] | ||
| Traffic state prediction, Support vector machine, Random forest, Long short-term memory, Intelligent transportation systems | ||
| مراجع | ||
|
[1] A.A. Faiyetole, A Review of Intelligent Transport System and Its People's Needs Considerations for Traffic Management's Policy Framework in a Developing Country: People's Needs Considerations for ITS Policy, Global Advancements in Connected and Intelligent Mobility: Emerging Research and Opportunities, (2020) 166-195. [2] X. Chen, J. Lu, J. Zhao, Z. Qu, Y. Yang, J. Xian, Traffic flow prediction at varied time scales via ensemble empirical mode decomposition and artificial neural network, Sustainability, 12(9) (2020) 3678. [3] C.D. Harper, S. Qian, C. Samaras, Improving Short-Term Travel Speed Prediction with High-Resolution Spatial and Temporal Rainfall Data, Journal of Transportation Engineering, Part A: Systems, 147(3) (2021) 04021004. [4] D. Xu, C. Wei, P. Peng, Q. Xuan, H. Guo, GE-GAN: A novel deep learning framework for road traffic state estimation, Transportation Research Part C: Emerging Technologies, 117 (2020) 102635. [5] A. Pankratz, Forecasting with univariate Box-Jenkins models: Concepts and cases, John Wiley & Sons, 2009. [6] 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) 1-9. [7] D. Yan, J. Zhou, Y. Zhao, B. Wu, Short-term subway passenger flow prediction based on ARIMA, in: International Conference on Geo-Spatial Knowledge and Intelligence, Springer, 2017, pp. 464-479. [8] B.L. Smith, B.M. Williams, R.K. Oswald, Comparison of parametric and nonparametric models for traffic flow forecasting, Transportation Research Part C: Emerging Technologies, 10(4) (2002) 303-321. [9] 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. [10] B. Sun, W. Cheng, P. Goswami, G. Bai, Flow-aware WPT k-nearest neighbours regression for short-term traffic prediction, in: 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2017, pp. 48-53. [11] F.I. Rahman, SHORT TERM TRAFFIC FLOW PREDICTION USING MACHINE LEARNING-KNN, SVM AND ANN WITH WEATHER INFORMATION, International Journal for Traffic & Transport Engineering, 10(3) (2020). [12] Y. Li, M. Chen, X. Lu, W. Zhao, Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system, Science China Technological Sciences, 61(5) (2018) 782-790. [13] Y. Liu, H. Wu, Prediction of road traffic congestion based on random forest, in: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), IEEE, 2017, pp. 361-364. [14] R. CHENG, M.-M. ZHANG, Y. Xue-Mei, Prediction Model for Road Traffic Accident Based on Random Forest, DEStech Transactions on Social Science, Education and Human Science, (icesd) (2019). [15] B. Sharma, S. Kumar, P. Tiwari, P. Yadav, M.I. Nezhurina, ANN based short-term traffic flow forecasting in undivided two lane highway, Journal of Big Data, 5(1) (2018) 1-16. [16] A. Raza, M. Zhong, Lane-based short-term urban traffic forecasting with GA designed ANN and LWR models, Transportation research procedia, 25 (2017) 1430-1443. [17] N. Ramakrishnan, T. Soni, Network traffic prediction using recurrent neural networks, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2018, pp. 187-193. [18] Z. Zhao, W. Chen, X. Wu, P.C. Chen, J. Liu, LSTM network: a deep learning approach for short-term traffic forecast, IET Intelligent Transport Systems, 11(2) (2017) 68-75. [19] N. Ranjan, S. Bhandari, H.P. Zhao, H. Kim, P. Khan, City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN, IEEE Access, 8 (2020) 81606-81620. [20] M. Zahid, Y. Chen, A. Jamal, M.Q. Memon, Short term traffic state prediction via hyperparameter optimization based classifiers, Sensors, 20(3) (2020) 685. [21] 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. [22] G. Rutka, Network traffic prediction using ARIMA and neural networks models, Elektronika ir Elektrotechnika, 84(4) (2008) 53-58. [23] A.M. Khoei, A. Bhaskar, E. Chung, Travel time prediction on signalised urban arterials by applying SARIMA modelling on Bluetooth data, in: 36th Australasian transport research forum (ATRF) 2013, 2013. [24] N.K. Chikkakrishna, C. Hardik, K. Deepika, N. Sparsha, Short-term traffic prediction using sarima and FbPROPHET, in: 2019 IEEE 16th India Council International Conference (INDICON), IEEE, 2019, pp. 1-4. [25] T. Alghamdi, K. Elgazzar, M. Bayoumi, T. Sharaf, S. Shah, Forecasting traffic congestion using ARIMA modeling, in: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, 2019, pp. 1227-1232. [26] V. Eramo, T. Catena, F. Lavacca, F. Di Giorgio, Study and investigation of SARIMA-based traffic prediction models for the resource allocation in NFV networks with elastic optical interconnection, in: 2020 22nd International Conference on Transparent Optical Networks (ICTON), IEEE, 2020, pp. 1-4. [27] C. Goves, R. North, R. Johnston, G. Fletcher, Short term traffic prediction on the UK motorway network using neural networks, Transportation Research Procedia, 13 (2016) 184-195. [28] H. Yu, Z. Wu, S. Wang, Y. Wang, X. Ma, Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks, Sensors, 17(7) (2017) 1501. [29] G. Yang, Y. Wang, H. Yu, Y. Ren, J. Xie, Short-term traffic state prediction based on the spatiotemporal features of critical road sections, Sensors, 18(7) (2018) 2287. [30] Z. Abbas, A. Al-Shishtawy, S. Girdzijauskas, V. Vlassov, Short-term traffic prediction using long short-term memory neural networks, in: 2018 IEEE International Congress on Big Data (BigData Congress), IEEE, 2018, pp. 57-65. [31] B. Sun, W. Cheng, P. Goswami, G. Bai, Short-term traffic forecasting using self-adjusting k-nearest neighbours, IET Intelligent Transport Systems, 12(1) (2017) 41-48. [32] L. Qu, W. Li, W. Li, D. Ma, Y. Wang, Daily long-term traffic flow forecasting based on a deep neural network, Expert Systems with applications, 121 (2019) 304-312. [33] C. Bratsas, K. Koupidis, J.-M. Salanova, K. Giannakopoulos, A. Kaloudis, G. Aifadopoulou, A comparison of machine learning methods for the prediction of traffic speed in urban places, Sustainability, 12(1) (2020) 142. [34] Z. Wang, X. Su, Z. Ding, Long-term traffic prediction based on lstm encoder-decoder architecture, IEEE Transactions on Intelligent Transportation Systems, (2020). [35] Y.N. Malek, M. Najib, M. Bakhouya, M. Essaaidi, Multivariate deep learning approach for electric vehicle speed forecasting, Big Data Mining and Analytics, 4(1) (2021) 56-64. [36] S. Yang, S. Qian, Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents, IEEE Intelligent Transportation Systems Magazine, 11(3) (2019) 12-28. [37] C. Zheng, X. Fan, C. Wen, L. Chen, C. Wang, J. Li, Deepstd: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction, IEEE Transactions on Intelligent Transportation Systems, 21(9) (2019) 3744-3755. [38] D. Zhang, M.R. Kabuka, Combining weather condition data to predict traffic flow: a GRU-based deep learning approach, IET Intelligent Transport Systems, 12(7) (2018) 578-585. [39] J. Wang, W. Zhu, Y. Sun, C. Tian, An effective dynamic spatiotemporal framework with external features information for traffic prediction, Applied Intelligence, (2020) 1-15. [40] M. Ni, Q. He, J. Gao, Using social media to predict traffic flow under special event conditions, in: The 93rd annual meeting of transportation research board, 2014. [41] X. Yang, Y. Yuan, Z. Liu, Short-term traffic speed prediction of urban road with multi-source data, IEEE Access, 8 (2020) 87541-87551. [42] C. Ranjan, M. Reddy, M. Mustonen, K. Paynabar, K. Pourak, Dataset: rare event classification in multivariate time series, arXiv preprint arXiv:1809.10717, (2018). [43] D.A. Pisner, D.M. Schnyer, Support vector machine, in: Machine Learning, Elsevier, 2020, pp. 101-121. [44] G. Biau, E. Scornet, A random forest guided tour, Test, 25(2) (2016) 197-227. [45] R.C. Staudemeyer, E.R. Morris, Understanding LSTM--a tutorial into Long Short-Term Memory Recurrent Neural Networks, arXiv preprint arXiv:1909.09586, (2019). [46] D.M. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, arXiv preprint arXiv:2010.16061, (2020). [47] 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. [48] 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. | ||
|
آمار تعداد مشاهده مقاله: 2,695 تعداد دریافت فایل اصل مقاله: 2,624 |
||