Intelligent Predictive Model for Road Traffic Congestion and Monitoring System: A Systematic Survey

K Dhananjaya Kumar (1), M. L. Anitha (2), M N Veena (3)
(1) Vidya Vikas Institute of Engineering and Technology, Visvesvaraya Technological University, Karnataka, India
(2) P E S College of Engineering, Visvesvaraya Technological University, Karnataka, India
(3) P E S College of Engineering, Visvesvaraya Technological University, Karnataka, India
Fulltext View | Download
How to cite (IJASEIT) :
Citation Format :

Traffic Congestion is major problem in many cities due to increasing more number of vehicles, low
maintenance of traffic signal and lack of infrastructure. So traffic jams are one of the major serious
problem leading to environmental pollution, fuel, economy, time wastage, and also serious impact
of human health issues. The prediction of road traffic congestion is most essential while uses of
intelligent predictive model technique. Based on this survey, to find the solution of these problem
using artificial intelligence and various machine learning algorithms.

Yi Liu, Xuesong Feng, et al.,”Prediction of urban road congestion using a bayesian network approach”, Procedia-social and Behavioral sciences 138 (2014) 671-678.

Toshio Ito and Ryohei Kaneyasu ,“Predicting traffic congestion using driver behavior”, Procedia computer science 112 (2017) 1288-1297.

Suguna Devi and T. Neetha, “Machine Learning based traffic congestion prediction in a IOT based smart city”, International Research journal of Engineering and Technology, Vol 04, Issue:05, may-2017.

Pallavi A. Mandhare, Vilas Kharat, et al.,”Intelligent Road Traffic Control System for Traffic Congestion A Perspective”, International journal of computer sciences and Engineering, Vol-6,Issue-7,July-2018, EISSN:2347-2693.

Mirialys Machin, Julio A. Sanguesa, et al., ”On the use of artificial intelligence techniques in Intelligent Transportation Systems” April-2018, https://www.researchgate.net/publication/325494956.

Jason Kurniawan, Sensa G. S. Syahra, et al.,”Traffic congestion Detection: Learning from CCTV Monitoring Image using Convolutional Neural Network”,Procedia computer science 144 (2018) 291-297.

S. Narmadha and Dr. V. Vijayakumar, “A Novel Framework for traffic flow prediction with deep learning algorithms”, International Journal of Engineering Research in computer science and Engineering vol 5, Issue 4, April 2018.

T Manoranjitham and Poornima Raj, “A Survey of road traffic prediction with deep learning”, International Journal of pure and applied mathematics, Vol 120, No.6 2018, 2065-2073.

Tin T. Nguyen, Panchamy Krishnakumari, et al., “Feature extraction and clustering analysis of highway congestion”, Transportation Research Part C 100 (2019) 238-258.

E. Heyns, S. Uniyal, et al.,”Predicting traffic phases from car sensor data using Machine Learning”, Procedia Coputer Science 151 (2019) 92-99.

Nadia SLIMANI, Iiham SLIMANI, et al., “Traffic forecasting in Morocco using artificial neural networks”, Procedia Computer Science 151 (2019) 471-476.

Williams Ackaah,”Exploring the use of advanced traffic information system to manage Traffic congestion in developing countries”,Scientific African 4 (2019) e00079.

Shridevi Jeevan Kamble and Manjunath R Kounte,”Machine Learning approach on traffic congestion monitoring system in Internet of Vehicles”,Procedia computer science 171 (2020) 2235-2241.

Obed Appiah, Ebenezer Quayson, et al.,”Ultrasonic sensor based traffic information acquisition system; a cheaper alternative For ITS application in developing countries”,Scientific African 9 (2020) e00487.

I.O. Olayod, L.K Tartibu, et al., “Intelligent transportation systems, un-signalized road intersections and traffic congestion In Johannesburg: a Systemativ review”,Procedia CIRP 91 (2020) 844-850.

John F. Zaki, Amr Ali-Eldin, et al.,”Traffic congestion prediction based on Hidden Markov Models and contrast Measure”,Ain shams Engineering Journal 11 (2020) 535-551.

Xueyan Yin, Genze Wu, et al., “Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions”, ArXiv:2004.08555v4 [eess.SP] 19 mar 2021.

Yue Hou, Jiaxing Chen, et al.,”The effect of the dataset on evaluating urban traffic prediction”, Alexandria Engineering Jounal (2021) 60, 597-671.

Nishant Kumar, Martin Raubal,” Applications of deep learning in congestion detection, prediction and Alleviation: A survey”,Transportation Research Part C 133 (2021) 103432.

Xuexin Bao, Dan Jiang, et al.,”An improved deep belief network for traffic prediction considering weather factors”, Alexandria Engineering Jounal (2021) 60, 413-420.

Homa Taghipour, Amir Bahador Parse, et al., ”A novel deep ensemble based approach to detect crashes using sequential traffic data”,IATSS (2021) -00325.

Rami AI-Naim and Yuriy Lytkin, “Review and comparison of prediction algorithm for the estimated time of arrival geospatial transportation data”, Procedia Computer Science 193 (2021) 13-21.

Andre Broekman, Petrus Johnnes Gribe, et al.,”Real time traffic quantization using a mini edge artificial intelligence platform”, Transportation Engineering 4 (2021) 100068.

Zahra Karami and Rasha Kashef, “Smart transporatation planning: Data, models and algorithms”, Transportation Engineering 2 (2021) 100013.

Lakshmi Shankar Iyer, “AI enabled applications towards intelligent transportation”, Transportation Engineering 5 (2021) 100083.

Sara Zahedian, Amir Nohekhan, et al.,”Dynamic toll prediction using historical data on toll roads: case study of the I-66 Inner Beltway”, Transportation Engineering 5 (2021) 100084.

TomTom Traffic Index- Live traffic statistics and historical data.

Ministry of Road Transport and Highways https://morth.nic.in