Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features

• Autorzy: Muthukumaran Venkatesan , Natarajan Rajesh , Kaladevi Amarakundhi Chandrasekaran , Magesh Gopu , Babu Swapna

Identyfikatory

DOI

10.1007/s11600-022-00875-8

• Języki publikacji: EN

Abstrakty

EN

Modern civilization has reported a significant rise in the volume of traffic on inland rivers all over the globe. Traffic flow prediction is essential for a good travel experience, but adequate computer processes for processing unpredictable spatiotemporal data (timestamp, weather, vessel_ID, water level, vessel_position, vessel_speed) in the inland water transportation industry are lacking. Moreover, such type of prediction relies primarily on past traffic patterns and perhaps other pertinent facts. Thus, we propose a deep learning-based computing process, namely Convolution Neural Network-Long Short-Term Memory Network (CNN-LSTM), a progressive predictor of employing uncertain spatiotemporal information to decrease navigation mishaps, traffic and flow prediction failures during transportation. Spatiotemporal correlation of current traffic flow may be processed using a simplified CNN-LSTM model. This hybridized prediction technique decreases update costs and meets the prediction needs with minimal computing overhead. A short case study on the waterways of the Indian state of Assam from Sandiya (27.835090 latitude, 95.658590 longitude) to Dhubri (26.022699 latitude, 89.978401 longitude) is undertaken to assess the model's performance. The evaluation of the suggested method includes a variety of trajectories of water transportation vehicles, including ferries, sailing boats, container ships, etc. The suggested approach outperforms conventional traffic flow predicting methods when it comes to short-term prediction with minimal predictive error (<2.75) and exhibited a major difference of more than 45% on the comparison of other methods.

Słowa kluczowe

EN

deep learning; CNN-LSTM; traffic flow; prediction; waterways; RoI; RNN; optimizer; drop rate; relative error

PL

głęboka nauka; CNN-LSTM; ruch uliczny; prognoza; drogi wodne; RoI; RNN; optymalizator

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2979--2990
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Muthukumaran Venkatesan

• Department of Mathematics, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India

• https://orcid.org/0000-0002-3393-5596

autor

Natarajan Rajesh

• Department of Information Technology, University of Applied Science and Technology, Shinas, Sultanate of Oman

autor

Kaladevi Amarakundhi Chandrasekaran

• Department of Computer Science Engineering, Sona College of Technology, Salem, India

autor

Magesh Gopu

• School of Information Technology and Engineering, VIT University, Vellore, India

autor

Babu Swapna

• Department of Electronics and Communication Engineering, Dr. M.G.R. Educational and Research Institute, Chennai-95, India

Bibliografia

• . Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166. https://doi.org/10.1109/72.279181
• 2. Gu Y, Lu W, Xu X, Qin L, Shao Z, Zhang H (2020) An improved Bayesian combination model for short-term traffic prediction with deep learning. IEEE Trans Intell Transp Syst 21(3):1332–1342. https://doi.org/10.1109/tits.2019.2939290
• 3. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647
• 4. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527
• 5. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
• 6. Home | Inland Waterways Authority of India, Government of India (2022) iwai.nic.in. https://iwai.nic.in
• 7. Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201. https://doi.org/10.1109/TITS.2014.2311123
• 8. Karthick Raghunath KM, Thirukumaran S (2019) Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system. Automatika 60(5):547–554. https://doi.org/10.1080/00051144.2019.1643963
• 9. Kim BS, Kim TG (2019) Cooperation of simulation and data model for performance analysis of complex systems. Int J Simul Model 18(4):608–619. https://doi.org/10.2507/ijsimm18(4)491
• 10. Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65(12):9508–9517. https://doi.org/10.1109/tvt.2016.2585575
• 11. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791
• 12. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
• 13. Lipshitz R, Strauss O (1997) Coping with uncertainty: a naturalistic decision-making analysis. Organ Behav Hum Decis Process 69(2):149–163. https://doi.org/10.1006/obhd.1997.2679
• 14. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019
• 15. Lu W, Li J, Li Y, Sun A, Wang J (2020) A CNN-LSTM-based model to forecast stock prices. Complexity 2020:1–10. https://doi.org/10.1155/2020/6622927
• 16. Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/tits.2014.2345663
• 17. Nowy A, Łazuga K, Gucma L, Androjna A, Perkovič M, Srše J (2021) Modeling of vessel traffic flow for waterway design-port of Świnoujście case study. Appl Sci 11(17):8126. https://doi.org/10.3390/app11178126
• 18. Ove Hansson S (1996) Decision making under great uncertainty. Philos Soc Sci 26(3):369–386. https://doi.org/10.1177/004839319602600304
• 19. Pongpaibool P, Tangamchit P, Noodwong K (2007) Evaluation of road traffic congestion using fuzzy techniques. In: TENCON 2007–2007 IEEE region 10 conference. https://doi.org/10.1109/tencon.2007.4429119
• 20. Shankar H, Raju PLN, Rao KRM (2012) Multi model criteria for the estimation of road traffic congestion from traffic flow information based on fuzzy logic. J Transp Technol 02(01):50–62. https://doi.org/10.4236/jtts.2012.21006
• 21. Tian Y, Pan L (2015) Predicting short-term traffic flow by long short-term memory recurrent neural network. In: 2015 IEEE international conference on Smart City/SocialCom/SustainCom (SmartCity). https://doi.org/10.1109/smartcity.2015.63
• 22. Vol II presentation - iwai.nic.in (n.d.) Retrieved 16 May 2022, fromhttps://iwai.nic.in/sites/default/files/integreted_TPT_Presentation_1Of2-37458750.pdf
• 23. Xie H, Liu M, Chen S (2009) Forecasting model of short-term traffic flow for road network based on independent component analysis and support vector machine. J Comput Appl 29(9):2550–2553. https://doi.org/10.3724/sp.j.1087.2009.02550
• 24. Yu D, Liu Y, Yu X (2016) A data grouping CNN algorithm for short-term traffic flow forecasting. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-319-45814-4_8
• 25. Zarrad O, Hajjaji MA, Mansouri MN (2019) Hardware implementation of hybrid wind-solar energy system for pumping water based on artificial neural network controller. Stud Inform Control 28(1):35–44. https://doi.org/10.24846/v28i1y201904
• 26. Zhang X, Onieva E, Perallos A, Osaba E, Lee VCS (2014) Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction. Transp Res Part c: Emerg Technol 43:127–142. https://doi.org/10.1016/j.trc.2014.02.013
• 27. Zhao Z, Chen W, Wu X, Chen PCY, Liu J (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intel Transport Syst 11(2):68–75. https://doi.org/10.1049/iet-its.2016.0208

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).