Volume 39 Issue 3
Jun.  2021
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CHEN Jialiang, HU Zhaozheng, LI Fei. An Estimation Method of Traffic Flow State Based on Matching of Temporal-spatial Feature Sequences[J]. Journal of Transport Information and Safety, 2021, 39(3): 68-76, 120. doi: 10.3963/j.jssn.1674-4861.2021.03.009
Citation: CHEN Jialiang, HU Zhaozheng, LI Fei. An Estimation Method of Traffic Flow State Based on Matching of Temporal-spatial Feature Sequences[J]. Journal of Transport Information and Safety, 2021, 39(3): 68-76, 120. doi: 10.3963/j.jssn.1674-4861.2021.03.009

An Estimation Method of Traffic Flow State Based on Matching of Temporal-spatial Feature Sequences

doi: 10.3963/j.jssn.1674-4861.2021.03.009
  • Received Date: 2021-01-25
  • An estimation model of the traffic flow state based on matching of temporal-spatial feature sequences is studied to better estimate the traffic flow state for the road section without a traffic flow detector and improve the estimation accuracy.The model firstly uses the calculation method of the traffic-operation index to preset the traffic-flow state of the urban-road section with traffic flow data.Various factors affecting the operating conditions of urban roads are analyzed, with the introduction of the characteristics of time and space multi-dimensional parameters such as traffic flow parameters, road parameters, and road network topology parameters.The temporal and spatial characteristics of traffic flow form by extracting 3 dimensions, 8 features, and 1 additional dimension, thus constructing the DNA feature sequence of urban-road traffic flow.After normalizing the value of each feature, the WH-KNN matching method is used to obtain the traffic-flow state closest to the road section to be estimated in the whole road network.The experiment selects the data of one week in road sections 10468, 10483, and 8816 of Wuhan Zhonghuan Expressway.Assuming that the road section data is missing, the traffic flow state is estimated by the method described above, and the estimated results are compared with the original data results.The results shows that the model can obtain the traffic flow state of the road section without detection data as well as maintain the accuracy rate of the state estimation result above 88%. The misjudgment is within a performance-index level.

     

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