Vehicle abnormal deceleration region detecting method and system based on trajectory data
A technology of trajectory data and area detection, applied in the field of intelligent transportation, can solve the problems of low inspection efficiency and high delay
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Embodiment 1
[0082] Embodiment 1: as figure 1 As shown, a vehicle abnormal deceleration area detection system based on trajectory data includes a real-time trajectory module, a historical trajectory module, a deceleration confidence interval module, and an abnormality detection module; wherein, the real-time trajectory module and the historical trajectory module include raw data Acquisition unit, data preprocessing unit, trajectory correction unit, deceleration status screening unit, road section matching unit; the real-time trajectory module is connected to the abnormal detection module; the historical trajectory module is connected to the deceleration confidence interval module; the deceleration The confidence interval module is connected to the anomaly detection module.
[0083] A method for detecting an abnormal deceleration area of a vehicle based on trajectory data, the specific steps are:
[0084] S1. Obtain the original trajectory data of the vehicle and the geographical informa...
Embodiment 2
[0186] Embodiment 2: A method for detecting an abnormal deceleration region of a vehicle based on trajectory data, the specific steps are:
[0187] S1. Obtaining the original trajectory data of the vehicle and the geographical information data of the urban road network;
[0188] S2. Carry out map matching on the trajectory data, and identify the driving state of the vehicle.
[0189] S3. Screen the historical trajectory points in the deceleration state, and use a clustering algorithm to construct a deceleration confidence area.
[0190] S3.1. Obtain the matching results of all vehicle historical trajectory data, and filter deceleration points.
[0191] S3.2. Use the density-based clustering method to cluster the deceleration points and construct a historical deceleration confidence area. Density-based clustering methods include, but are not limited to: DBSCAN algorithm, SNN (shared nearest neighbor) algorithm, density peak-based algorithm DP (Clustering by fast search and fi...
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