Real-time evaluation method for road service level based on traffic model driven

Abstract

Aimed at the road service level prediction problem for real-time traffic flow, a real-time evaluation method for road service level based on traffic model driven is proposed. Firstly analyze the basic feature model of traffic flow, using flow-time occupancy model as a reference model for congestion assessment, and based on K-means clustering algorithm to complete traffic-based congestion definition. Then use the BP neural network algorithm to build congestion assessment model, finally establish a real-time stream processing framework based on Spark Streaming to realize real-time evaluation for road service level. The experiment results show that the method could effectively describe the state of congestion, and be able to evaluate road service in real time based on traffic flow data, with decision support for intelligent traffic control system to improve the service level.