Dynamic traffic district division method based on trip data

Abstract

The invention discloses a dynamic traffic district division method based on trip data. The method comprises the following steps that S1) a division area is made clear, and trip data including origin and destination attribution information of a trip of the division area is obtained; S2) valid trip data is extracted via the trip data in the S1 according to practical demands; S3) origin and destination of the trip and corresponding trip quantities are obtained according to the valid trip data; S4) a trip network of the division area is constructed via the origin and destination of the trip and corresponding trip quantities in the S3; and S5) the division area is divided into traffic districts in a community discovery algorithm by combining the trip network of the S4. Thus, the data bulk is large by taking the large scale of trip data as the basis, the time and space scope of research is wide, trip activities of the whole urban road network can be reflected comprehensively and vividly, andan analysis result approaches the reality more; and the corresponding trip data is obtained according to the practical demands to realize multi-level dynamic division of the specific traffic district, and the method is more flexible and targeted.

Description

technical field [0001] The invention relates to the technical field of traffic engineering, and more specifically, to a method for dynamically dividing traffic areas based on travel data. Background technique [0002] A traffic area refers to a collection of nodes and links with a certain degree of traffic correlation and traffic similarity, and there are certain differences in the spatio-temporal variation characteristics of traffic in different traffic areas. In recent years, with the continuous acceleration of the urbanization process, the scale of urban roads is also increasing, the road network is criss-crossed, and the traffic network is becoming more and more complex. In order to enhance the operability of traffic surveys, it is necessary to reduce the traffic survey workload and reduce Due to the complexity of traffic prediction analysis, dividing the entire urban traffic network into several traffic areas, and then performing relevant traffic analysis for each area ...

AI Technical Summary

Problems solved by technology

All the above methods have certain limitations. They are greatly affected by the physical facilities in the road network, and are easily affected by factors such as administrative divisions and natural barriers. Manual intervention is required, and there is strong subjectivity, which makes the representativeness and persuasiveness of the classification results not strong

In addition, the division process of the traffic area is relatively mechanical, and it is difficult to adjust flexibly according to the actual situation, that is, the dynamic division of the traffic area cannot be realized

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