| Urban expressways are roads that serve cross regional traffic needs,alleviate regional traffic pressure,and enhance the overall capacity of the road network.However,due to rapid economic development,severe separation of work and residence,and increasing demand for cross regional travel,the traffic pressure on urban expressways has sharply increased.Especially during peak hours,severe congestion often occurs in bottleneck sections of the ramp weaving area,resulting in a decrease in road capacity and travel speed,and the expressway becoming a "slow road".How to alleviate traffic congestion on existing urban expressways has become an urgent problem for many cities.In recent years,floating car technology has rapidly developed with the help of the new generation of information and communication industry.Floating cars can accurately and realtime transmit vehicle position and status data,becoming an important data source for intelligent transportation control with the advantages of low cost,high efficiency,and wide coverage.At the same time,many existing expressways lack traffic detection equipment on the main line and ramps,making it difficult to collect traffic parameters.Existing ramp control methods are difficult to implement,and the processing and analysis of floating car positioning data provide new solutions to alleviate urban expressway congestion.This article aims at practical application and constructs an active ramp control system for urban expressways based on travel speed,based on a large amount of real-time positioning data of floating cars gathered by the traffic brain.The main research content is as follows:(1)Establish a data processing model for urban expressway floating vehicle positioning based on massive raw floating vehicle positioning data.Firstly,this paper establishes the floating car positioning data preprocessing mechanism,including data cleaning,outlier detection,and road matching;By using clustering algorithms to determine the location of vehicles on urban expressways,fast,accurate,and efficient extraction of vehicle data on elevated bridge expressways and ground vehicles has been achieved.(2)In order to achieve accurate prediction of travel speed,a travel speed prediction model for urban expressways based on TSA-GRU is proposed.Extract relevant features for travel speed prediction by analyzing the temporal and spatial correlations of travel speed;Taking into account the temporal and spatial attention mechanisms,an improved urban expressway travel speed prediction model is proposed,fully exploring the temporal and spatial dependencies between travel speed prediction features,and improving the accuracy of travel speed prediction.(3)Establish an active ramp control system based on travel speed to address traffic congestion on urban expressways.The system integrates floating vehicle travel speed data,uses a travel speed prediction model to determine the heuristic conditions for active ramp control and identify abnormal congestion events.It adopts a deep reinforcement learning active ramp control method,which can adopt control strategies before congestion occurs,balancing the operating conditions of the main traffic on the expressway and the queue length of the ramp.This paper demonstrates through simulation experiments that the travel speed data obtained using floating positioning data can achieve active ramp control of urban expressway entrance ramps.Compared with the uncontrolled situation,the traffic flow increased by 123 vehicles per hour,the average occupancy rate decreased by 11.27%,the average travel speed increased by 9.62%,the total travel time decreased by 13.71%,and the total delay of the road network decreased by 35.01%.At the same time,compared to traditional ramp control methods,the average queue length of the ramp decreased by 15.72%.The validated active ramp control system was piloted and applied to the South Expressway of the Second Ring Road in Jinan City.Practical data shows that the system can effectively alleviate traffic congestion in bottleneck sections of the entrance ramp and improve the traffic operation status of the urban expressway. |
