The analysis of the traffic flow of the intersection based on the queuing theory. In: 2013 IEEE 8th international symposium on applied computational intelligence and informatics (SACI) In: IEEE CLOUD, 2013ĭan P (2013) Urban traffic congestion prediction based on routes information. Li F et al (2013) Efficient and scalable IoT service delivery on cloud. In: TRB 91st annual meeting, Washington D.C., Jan 2012 Jain V, Sharma A, Dhananjay A, Subramanian L (2012) Traffic density estimation for noisy camera sources. In: The third international conference on advances in system simulation In: 2015 17th UKSIMAMSS international conference on modelling and simulationīehrisch M, Bieker L, Erdmann J, Krajzewicz D (2011) SUMO-simulation of urban mobility. Springer, BerlinĪbidin AF, Kolberg M (2015) Towards improved vehicle arrival time prediction in public transportation: integrating SUMO and Kalman Filter models. Stanford University, īacon J, Bejan AI, Beresford AR, Evans D, Gibbens RJ, Moody K (2011) Using real-time road traffic data to evaluate congestion. Final project, CS 229 (Machine Learning). Glick J (2015) Reinforcement learning for adaptive traffic signal control. Nayak RR, Sahana SK, Bagalkot AS, Soumya M, Roopa J, Govinda Raju M, Ramavenkateswaransmart N (2013) Traffic congestion control using wireless communication.
Predicting traffic congestion with driving behavior. Sarath S, Chinnu R, Gopika PS (2016) Real time smart traffic control system using dynamic background. Misbahuddin S, Zubairi JA, Saggaf A, Basuni J, Wadany SA, Al-Sofi A (2014) IoT based dynamic road traffic management for smart cities Simulation of genetic algorithm: traffic light efficiency. KeywordsĪtote BS, Bedekar M, Panicker SS (2015) Traffic signal control for urban area. By using this method the overall waiting time of vehicles considerably reduced. It considers inflow and out flow of traffic at each lines and also waiting time of the vehicle for scheduling the signal timings. The proposed work adopts to optimize a standard traffic a junction of two roads, one with North–South orientation and other with East–West orientation stop light dynamically with reinforcement learning and with markov decision process. So there is a pressing need of efficient algorithms for congestion prediction by considering historical and real time traffic data. Static phase timing is not an optimal solution for reducing the congestion at the signals. This package is great due to the fact it reads in an xml string and converts it to a far easier to use json structure. Dynamic scheduling of the signals is renowned as a solution for traffic congestion mitigation in urban areas. In this tutorial I’ll be demonstrating how you can easily manipulate XML files using the xml2js node package.
A fixed duration traffic light in an intersection can make a few empty lanes and at the same time create other congested lanes. As the advancement of vehicular traffic, traffic congestion alleviation is desperately required in urban cities.
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