Method for estimating city expressway traffic states based on mobile detection of smartphones

Abstract

The invention discloses a method for estimating city expressway traffic states based on mobile detection of smartphones, wherein a city expressway cell transmission model is built firstly, a observing network is built by adopting a smartphone to rapidly detect parameters, then a state space model based on a lighthill-whitham-richards (LWR) traffic flow model is designed, a traffic state and a boundary flux are synchronously estimated by utilizing three-step type recursive filters algorithm, then are coalesced with upstream and downstream subsection boundary flux by adopting a weighted average algorithm, and traffic parameter estimation is upgraded, thereby achieving real-time distributed estimation of city expressway network traffic state. The method for estimating the city expressway traffic states based on the mobile detection of the smartphones can collect average speed information of vehicles on any time and space positions of a loop, enables traffic estimation not to be restrained by the position of a detector, can achieve synchronization estimation of traffic density and boundary flux by designing a state-space model and a three-step recursive filter, and achieves the problem of large range expressway network traffic estimation by being coalesced with subsection boundary flux, reduces model order, and improves efficiency of algorithm.

Description

technical field [0001] The invention belongs to the field of intelligent traffic systems, and relates to traffic data acquisition technology, a cell transmission model, a first-order macroscopic traffic flow model and a distributed fusion algorithm. The invention combines the smart phone-based traffic data acquisition technology, the first-order macroscopic traffic flow model, the Kalman filter and the distributed fusion algorithm to realize the real-time estimation of the distributed traffic state of the urban expressway. Background technique [0002] With the development of social economy, the speed of urbanization and motorization is accelerating, traffic congestion, traffic accidents, environmental pollution, energy shortage and other issues have become common problems faced by countries all over the world. The research on relieving road traffic congestion and improving traffic management service level by implementing effective traffic control and guidance is becoming mo...

AI Technical Summary

Problems solved by technology

[0005] The main factors affecting the estimation of urban expressway traffic state include two points. One is the boundary flow problem. The dynamics of the traffic flow model are jointly determined by the current traffic state of the system and the boundary flow. With the traffic data collection technology based on mobile phone data, the The boundary flow is usually unknown; the second is the order of the system model. The urban expressway network is usually divided into hundreds of cells. When the Kalman filter algorithm is used for estimation, the order of the system model is relatively high, and the amount of calculation is large, which is difficult to satisfy real-time requirements

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