Distributed System Based on Deep Learning for Vehicular Re-routing and Congestion Avoidance

The excessive growth of the population in large cities has created great demands on their transport systems. The congestion generated by public and private transport is the most important cause of air pollution, noise levels and economic losses caused by the time used in transfers, among others. Over the years, various approaches have been developed to alleviate traffic congestion. However, none of these solutions has been very effective. A better approach is to make transportation systems more efficient. To this end, Intelligent Transportation Systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. This work proposes a predictive congestion avoidance by re-routing system that uses a mechanism based on Deep Learning that combines real-time and historical data to characterize future traffic conditions. The model uses the information obtained from the previous step to determine the zones with possible congestion and redirects the vehicles that are about to cross them. Alternative routes are generated using the Entropy-Balanced kSP algorithm (EBkSP). The results obtained from simulations in a synthetic scenario have shown that the proposal is capable of reducing the Average Travel Time (ATT) by up to 7%, benefiting a maximum of 56% of the vehicles.

[1]  Antonio Alfredo Ferreira Loureiro,et al.  Real-time path planning to prevent traffic jam through an intelligent transportation system , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).

[2]  Karim Faez,et al.  PersianGulf: An Autonomous Combined Traffic Signal Controller and Route Guidance System , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[3]  Azzedine Boukerche,et al.  ICARUS: Improvement of traffic Condition through an Alerting and Re-routing System , 2016, Comput. Networks.

[4]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[5]  Azzedine Boukerche,et al.  An intelligent transportation system for detection and control of congested roads in urban centers , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).

[6]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[7]  Jennifer McManis,et al.  Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion , 2016, IEEE Transactions on Intelligent Transportation Systems.

[8]  Jorge Adolfo Ramírez Uresti,et al.  A Parallelized Algorithm for a Real-Time Traffic Recommendations Architecture Based in Multi-agents , 2014, MICAI.

[9]  Bhaskar Krishnamachari,et al.  Enhancing intelligence in inter-vehicle communications to detect and reduce congestion in urban centers , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).

[10]  Jennifer McManis,et al.  A Multi-Agent based vehicles re-routing system for unexpected traffic congestion avoidance , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[11]  Rodolfo Ipolito Meneguette,et al.  Scorpion: A Solution Using Cooperative Rerouting to Prevent Congestion and Improve Traffic Condition , 2015, CIT/IUCC/DASC/PICom.

[12]  Xiao Fan Wang,et al.  Efficient Routing on Large Road Networks Using Hierarchical Communities , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Victor J. Blue,et al.  A COOPERATIVE MULTI-AGENT TRANSPORTATION MANAGEMENT AND ROUTE GUIDANCE SYSTEM , 2002 .

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  K. R. Rao,et al.  Measuring Urban Traffic Congestion - A Review , 2012 .

[16]  Karine Zeitouni,et al.  Proactive Vehicle Re-routing Strategies for Congestion Avoidance , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[17]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[18]  Javier Gozálvez,et al.  Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications , 2013, J. Netw. Comput. Appl..

[19]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[20]  Selvaraj Vasantha Kumar,et al.  Traffic Flow Prediction using Kalman Filtering Technique , 2017 .

[21]  Hossein Jula,et al.  Vehicle Route Guidance Systems: Classification and Comparison , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[22]  Antonio Alfredo Ferreira Loureiro,et al.  CARTIM: A proposal toward identification and minimization of vehicular traffic congestion for VANET , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[23]  Chih-Wei Yi,et al.  iTraffic: A Smartphone-based Traffic Information System , 2013, 2013 42nd International Conference on Parallel Processing.

[24]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.