Intelligent Traffic Control Using Chronological Flow Analysis of Signal Gaps
Keywords:
Intelligent transportation system, Urban area, Traffic analysis, Sequential flow, and VMCMCAbstract
Urban areas grapple with daily traffic challenges due to the burgeoning number of vehicles, overwhelming roadway capacities. This leads to issues like congestion, delays, and pollution, negatively affecting society. Although Smart Traffic Management Systems (STMS) have been implemented to mitigate these problems, they encounter constraints. This research focuses on traffic control by identifying congested areas and proposing improvements. Through Sequential Flow Analysis (SFA) and Vehicle Markov-chain Monte Carlo (VMCMC) methods, it assesses traffic flow dynamics, aiming to minimize waiting times. The approach emphasizes roadside-based traffic control measures and infrastructure-based equipment to enhance traffic management, flow, throughput, accuracy, travel time, and pollution control. By monitoring congested sequences and optimizing signal timing, the study seeks to alleviate traffic issues effectively. The utilization of efficient inference tools in time-series data analysis enhances system performance by directing attention from vehicles to signals and expediting sensor data transmission.
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