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Constrained Markov Decision Processes for Intelligent Traffic

Publication Type : Conference Paper

Publisher : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE

Source : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE (2019)

Url : https://ieeexplore.ieee.org/abstract/document/8944574

Keywords : Approximation algorithms, Bangalore roads, busy roads, Computer simulation, Constraint MDP, decision making, given reinforcement learning problem statement, intelligent Traffic, Junctions, learning (artificial intelligence), markov decision process, Markov processes, Policy Gradient, proficient signaling systems, Reinforcement learning, road traffic, roads, Timing, TLC, Traffic congestion, traffic data, traffic engineering computing, Traffic Flow and Congestion Factor, vehicular traffic

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2019

Abstract : Traffic congestion has become a serious problem as most of the roads are busy and has to suffer vehicular traffic at an increasing rate. Proficient Signaling Systems are indispensable for reducing traffic congestion at intersections. This work tries to design a model according to constraint markov decision process for a given reinforcement learning problem statement which prediction of traffic congestion at the intersection and lanes in a busy roads. A Green Light Simulator is used to simulate real time data set of the Bangalore Roads and Traffic. After that several TLC algorithms are run and efficiency is computed manually. In the third step policy is build using parameters that contain features like acceleration, velocity, no of vehicles plying on the road. Based on these parameters, the policy generated helps to predict congestion at the busy roads. The dataset includes traffic data from Bangalore Roads.

Cite this Research Publication : Dr. Tripty Singh, “Constrained Markov Decision Processes for Intelligent Traffic”, in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019.

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