Trunk road phase difference optimizing method based on floating car trajectory data

Abstract

The invention discloses a trunk road phase difference optimizing method based on floating car trajectory data. The method aims to increase the number of vehicles passing a stop line during a green light period based on aggregated trajectory data so as to obtain an optimal phase difference, overcoming the defect of acquiring traffic information through a fixed detector in the prior art. The invention provides a high-precision, low-cost trunk road phase difference optimizing method. According to the method, trajectory data is aggregated and screened based on floating car trajectory data at first; then, the trajectory data is classified according to car motion states, and the number of cars passing the stop line at different green light periods is calculated by obtaining a car arrival curve;and finally, a trunk road optimal phase difference is obtained based on the largest number of cars passing the stop line during the green light period.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, in particular to a method for optimizing phase difference of arterial roads based on track data of floating cars. Background technique [0002] With the development of positioning technology, mobile network technology and wireless communication technology, it is more and more common to use intelligent terminal equipment to collect vehicle trajectories, which provides a new solution for traffic monitoring and traffic forecasting. Different from the disadvantages of traditional fixed detectors such as low coverage, high installation and maintenance costs, and high damage rate, smart terminal equipment can not only collect data in two dimensions of time and space, but also collect data in a large area at a relatively low communication cost. Detailed vehicle trajectories. Therefore, for the phase difference optimization of urban arterial roads, the floating car trajectory data can provide m...

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Problems solved by technology

[0003] At present, there have been relevant researches on optimizing the phase difference of urban arterial roads based on trajectory data. Wu has developed a GPS tool that can obtain the actual driving speed of vehicles by drawing trajectory data in the space-time diagram, and then use an improved bandwidth optimization algorithm to calculate the actual vehicle speed. Speed ​​adjustment phase difference and phase sequence; Priemer and Bernhard based on dynamic programming and full enumeration method to process the trajectory data of every 5s time interval to predict the queue length in the next 20s, and then use the predicted queue length to reduce the intersection queue length as the goal Optimize the phase sequence; by obtaining the functional relationship between the phase difference and the number of stops, Huang et al. established a phase difference optimization model with the optimization goal of minimizing the total number of stops of vehicles in the coordinated direction. This method is only applicable to arterial intersections under unsaturated conditions. Optimizing the phase difference of the mouth, which has great limitations in practicality

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