Method for data collection and OD (Origin-Destination) analysis of public transport passenger flow based on WIFI identity recognition

Abstract

The invention provides a method for the data collection and the OD (Origin-Destination) analysis of public transport passenger flow data based on WIFI (Wireless Fidelity) identity recognition. The method comprises the following steps: arranging an AP (Access Point) hot spot on a bus, reading the MAC (Media Access Control) addresses of WIFI equipment which are held by passengers, utilizing the MAC addresses to recognize the identities of the passengers, and storing the MAC addresses in a MAC address list in an AP; obtaining the total number of the WIFI passengers getting on / off the bus and the WIFI passengers on a cross section through the statistics of the MAC addresses in the list, and obtaining the total number of the passengers getting on / off each station and the total number of the passengers on the bus after the bus is driven away from the station through mathematic operation; establishing a detailed information database of the passengers taking the bus, storing data in a data table, and obtaining an OD matrix between all bus stations in the whole city through the query and the analysis of the data table. The real-time statistics and OD analysis of the passenger flow are combined, and the results are more real. The accuracy rate becomes increasingly higher. With the gradual increase of the popularization rate of intelligent mobile phones, the accuracy rate of the system is also gradually increased.

Description

technical field [0001] The invention provides a method for data collection, specifically, a method for data collection and OD analysis of bus passenger flow based on WIFI identification. Background technique [0002] At present, there are mainly the following methods for obtaining bus passenger flow data collection and OD information: 1) Manual collection: the starting point and destination are recorded by a special person or the passenger himself, such as the small ticket method, etc. The disadvantage is that it is not only time-consuming and laborious, but also accurate counting The rate is easily affected by artificial subjective factors; 2) Use automatic passenger flow counting equipment: including pedal type, infrared type, video analysis and other counting methods. These methods are more accurate when the passenger flow is small, but when the passenger flow is peak, the accuracy rate is relatively low, and can only identify the number of people getting on and off the b...

AI Technical Summary

Problems solved by technology

All these methods are assumed to be carried out under some ideal conditions, so the results may be quite different from the actual ones

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