Bus route adjusting method based on historical passenger flow volume

Abstract

The invention relates to a bus route adjusting method based on historical passenger flow volume. The method comprises the following steps: traversing passenger flow volume time sub-sequences of all lengths and clustering by using correlation coefficients, wherein a clustering result forms two time sequence sets, namely a positive correlation cluster set and a negative correlation cluster set whichrespectively represent route stations with the same riding behavior trend and route stations with opposite riding behavior trends, and analyzing the clustering result to obtain macroscopic passengertravel behavior characteristics between the routes and between the stations. The riding requirements of passengers can be known, stations with the same or similar passenger flow volume change can be found, a basis is provided for laying a bus network, setting a new route and adjusting an existing route in a city, decision support is provided for route adjustment, bus dispatching can be improved, and more scientific and reasonable operation is achieved. The invention does not depend on a specific scene, is more universal, and can be used for public transportation networks of various large and medium cities.

Description

technical field [0001] The invention relates to the technical field of data analysis, and more specifically, relates to a bus line adjustment method based on historical passenger flow. Background technique [0002] The setting and optimization of the bus network is a complex system engineering, which is often affected by many factors, such as the limitation of objective factors such as hardware infrastructure, and the design defects of the bus network itself, such as unreasonable structure, unbalanced network development, Line transport capacity does not match passenger flow, etc., and the throughput of passenger flow is far below expectations. This has greatly affected the overall efficiency of urban public transport. Therefore, it is of great significance to improve the passenger flow throughput of the entire bus network by mining the change trend of passenger flow between stations and lines and discovering the differences. [0003] For the research on the change trend o...

AI Technical Summary

Problems solved by technology

However, affected by the amount of data, the time complexity of traditional clustering methods is often very high, and it needs to be improved according to the characteristics of the data in order to better meet the actual analysis needs

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