Traffic anomaly detection method based on path travel time calculation

Abstract

The invention discloses a traffic anomaly detection method based on path travel time calculation. The method comprises the steps of (1) establishing a path travel time and road speed history database, (2) detecting an abnormal path, and (3) measuring influence on an abnormal path coverage road. According to the method, a local road anomaly (such as the increase of a single pass time caused by short stop of an individual taxi, and an anomaly which is caused by a short time irregularity of an individual vehicle and can be quickly dissipated by itself) caused by accidental factors can be filtered, if roads are connected and affect each other, the effects of multi-road collaborative influence can be superposed and amplified, and thus a good anomaly detection effect is obtained.

Description

technical field [0001] The invention relates to a traffic anomaly detection method based on path travel time calculation. Background technique [0002] Traffic anomaly detection has always been an important task in traffic management, and it is even more important in today's situation of implementing intelligent transportation systems. Among the existing traffic anomaly detection algorithms, the pattern recognition method is the most used method. It uses vehicle detectors to collect information such as lane occupancy, traffic density, and traffic speed. According to the designed algorithm, it identifies abnormal data and detects traffic anomalies. The use of pattern recognition methods often use traffic fixed coils, video, infrared sensing devices, etc., but the cost of collecting data with such devices is expensive and the flexibility is low. With the increasing popularity of the Global Positioning System (GPS), the tracking of moving objects has It has become a reality, ...

AI Technical Summary

Problems solved by technology

The use of pattern recognition methods often use traffic fixed coils, video, infrared sensing devices, etc., but the cost of collecting data with such devices is expensive and the flexibility is low. With the increasing popularity of the Global Positioning System (GPS), the tracking of moving objects has It has become a reality, and GPS data collection has the advantages of high precision, all-weather, high efficiency, and multi-function

Judging from the existing anomaly detection methods, the pattern recognition method is mostly based on the abnormal judgment of the road section, which can intuitively and clearly show the traffic situation of the road section to the pedestrian, but it is separated from the internal connection and interaction between the road sections.

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