Traffic congestion traceability method based on multi-source data

Abstract

The invention proposes a traffic congestion source tracing method based on multi-source data, and the method includes a multi-source data-based map matching algorithm (AVI-MM) and a multi-level traffic congestion source tracing algorithm. The AVI‑MM algorithm fuses sparse AVI data and massive GPS data. First, a set of vehicle path candidates is generated based on a random walk algorithm, then a priori probability is determined based on the Logit path selection model with the greatest utility, and a spatiotemporal fusion analysis is used to define the conditional probability. The sub-path with the highest probability of connection matching obtains the final full path; the multi-level traffic congestion source tracing algorithm calculates the OD distribution of multi-level vehicle trajectories of road sections, nodes and regions based on the map matching results. The method of the invention realizes the high-precision matching of the quasi-full-sample vehicle trajectory and the driving path, and based on the path matching result of the quasi-full-sample AVI data, quantitatively analyzes the starting and ending distribution and path distribution of the traffic flow in the congested road section.

Description

technical field [0001] The invention belongs to the field of urban traffic control and management, and in particular relates to a traffic congestion source tracing method based on multi-source data. Background technique [0002] Tracing the source of traffic congestion refers to tracing the starting and ending positions and driving paths of vehicles outside a certain spatial range, and providing strong support for the formulation of traffic control strategies by tracing the source and destination of traffic flow. [0003] Map matching is to calibrate the vehicle position information by representing road sections on a digital map. First, the sample vehicle trajectory data is matched to the road network through the map matching algorithm, and then the traffic flow sources passing through the observed road sections, intersections or areas can be obtained. And whereabouts, and ultimately realize the traceability of vehicle congestion. Therefore, map matching is an important tec...

AI Technical Summary

Problems solved by technology

These algorithms all show the problem of insufficient matching accuracy when facing sparse AVI data

[0005] Problems existing in the existing technology: On the one hand, the existing traffic congestion traceability methods mostly use GPS data, but there are limitations such as insufficient data sample size and location deviation, and it is impossible to realize traffic congestion traceability based on large-scale samples

On the other hand, although a quasi-full sample of traffic congestion can be traced by matching massive AVI trajectory data to the traffic network, there is a lack of map matching algorithms for sparse AVI data.

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