Urban traffic chain reconstruction method based on checkpoint video data

A technology of video data and urban transportation, applied in the field of intelligent transportation, can solve the problem of poor accuracy of vehicle travel trajectory restoration.

Active Publication Date: 2019-09-24
SOUTH CHINA UNIV OF TECH +2
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of poor accuracy of vehicle travel trajectory restoration in the prior art,

Method used

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  • Urban traffic chain reconstruction method based on checkpoint video data
  • Urban traffic chain reconstruction method based on checkpoint video data
  • Urban traffic chain reconstruction method based on checkpoint video data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0078] Such as figure 1 As shown, a method for reconstruction of urban traffic travel chain based on checkpoint video data includes the following steps:

[0079] Step S1: Collect the license plate data of vehicles passing through the bayonet through video, preprocess the collected license plate data, and extract the driving trajectory of each vehicle;

[0080] Step S2: Save the path detected by 4 or more checkpoints in each vehicle’s driving trajectory continuously as a historical path data set, and save the data detected by 2 discontinuous checkpoints in each vehicle’s driving trajectory as Trajectories that need to be restored;

[0081] Step S3: According to the PPA path algorithm, determine all possible driving trajectory sets T between the two bayonets of the trajectory to be restored i ;

[0082] Step S4: Calculate the possible driving trajectory set T corresponding to the historical route data set i The path travel time of each path, the minimum traffic flow of the r...

Embodiment 2

[0140] Such as figure 1 , figure 2 as well as image 3 As shown, in this embodiment, data statistics are performed every 30 minutes for 24 hours a day, and 48 time statistics windows can be obtained after division

[0141] According to the statistical time window, the collected data is preprocessed to obtain the sampled travel time samples, and the estimated travel time of the link under each statistical time window is estimated as follows:

[0142] First of all, by statistically analyzing the travel time data of road sections collected under the time window, noise processing is carried out for different situations of traffic congestion in a day;

[0143] First of all, for the travel time of road segments in different time periods, it should be satisfied that the travel time at this moment is greater than the free flow time, and set T l =T freeflow *α,T freeflow is the free flow time of the road section, in order to give a certain threshold space for urban road speeding ...

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Abstract

According to the invention, the method comprises the steps: employing a PPA path search algorithm (a potential path area path search algorithm) to construct an initial decision path; employing a decision attribute factor model training method to finally output a real path restored by the algorithm; selecting five decision optimization factors, namely path travel time, path length, path turning times and path controlled signal gate number, as decision attributes, so that the decision factors for track recovery have higher environmental adaptability. The setting of the decision weight is comprehensively determined by subjective and objective data, so the method is more scientific and practical, the algorithm speed is high, and large-scale data can be processed; the method is suitable for path reconstruction of small and medium-sized road networks, can complete restoration of missing detection vehicle tracks with high precision, has good robustness, and lays a foundation for further statistics of urban traffic road network microcosmic parameters.

Description

technical field [0001] The invention relates to the field of intelligent transportation, more specifically, to a method for reconstructing urban traffic travel chains based on checkpoint video data. Background technique [0002] With the deployment of advanced urban transportation facilities, the continuous changes and adjustments of traffic management methods and traffic operation models, the traditional traffic travel survey of residents consumes a lot of labor costs, financial resources and time costs, and the timeliness and accuracy are very low, which cannot meet the new requirements. The needs of traffic planning and management in the era. [0003] At present, the development momentum of big data storage and data mining technology in the field of intelligent transportation is in full swing. Big data storage and data mining technology is mainly based on basic traffic data, such as automatic number plate recognition data (Automatic Number Plate Recognition, ANPR), global...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/017
CPCG08G1/0104G08G1/0129G08G1/017
Inventor 魏鑫徐建闽林永杰首艳芳卢凯
Owner SOUTH CHINA UNIV OF TECH
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