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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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
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
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
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
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1918.104.22.1685
Home | Inland Waterways Authority of India, Government of India (2022) iwai.nic.in. https://iwai.nic.in
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
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
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
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
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
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
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
Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019
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
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
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
Ove Hansson S (1996) Decision making under great uncertainty. Philos Soc Sci 26(3):369–386. https://doi.org/10.1177/004839319602600304
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
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
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
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
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
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
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
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
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
Conflict of interest
The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Edited by Dr. V. Vinoth Kumar (GUEST EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).
About this article
Cite this article
Muthukumaran, V., Natarajan, R., Kaladevi, A.C. et al. Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features. Acta Geophys. 70, 2979–2990 (2022). https://doi.org/10.1007/s11600-022-00875-8