An accurate public transport passenger flow big data optimization method and device

Abstract

The invention discloses an accurate public transport passenger flow big data optimization method and device. The method comprises the following steps of (1) obtaining the passenger flow data of vehicles operating on the day and all classes of the vehicles, (2) determining departure interval intervals of all the classes of a certain vehicle, (3) determining the actual earliest passenger getting-ontime and the actual latest passenger getting-off time of passengers, and (4) optimizing the passenger flow big data. The earliest get-on time and the latest get-off time of the passenger are determined again through a computer program; by determining the circulating frequency, the ineffective passenger flow is removed, the passenger flow of getting on the bus in advance at the first station and getting off the bus after the last station is delayed is reserved, the accuracy of effective passenger flow big data of the bus is improved, the basic passenger flow big data support is provided for intelligent scheduling, passenger flow analysis and government planning, and meanwhile the feasibility of simulation application of bus network optimization is improved. In addition, the method is high in applicability and can be applied to various line types (such as loop lines and circulation lines).

Description

technical field [0001] The invention relates to the field of intelligent transportation research, especially the field of collection and application of accurate passenger flow big data, and specifically relates to an optimization method and device for accurate bus passenger flow big data. Background technique [0002] In order to alleviate traffic congestion, major cities in my country are actively planning and improving public transport services, carrying out research on bus line optimization and actual line network adjustment business, and passenger flow big data is a key factor in bus line network optimization, and the accuracy of passenger flow big data Determines whether the actual adjustment of the net optimization is feasible. [0003] At present, the most effective method of passenger flow collection is to collect the video images of passengers getting on and off the bus through the cameras at the front and rear doors of the bus, and use image recognition technology t...

AI Technical Summary

Problems solved by technology

[0003] At present, the most effective method of passenger flow collection is to collect the video images of passengers getting on and off the bus through the cameras at the front and rear doors of the bus, and use image recognition technology to realize the accurate collection of the number of people getting on and off the bus and the direction. Personnel, including invalid passenger flow, such as drivers getting on and off, vehicle cleaning personnel, maintenance personnel, maintenance personnel and other abnormal passengers getting on and off the bus; at the same time, most buses have the phenomenon of passengers getting on the bus ahead of time after arriving at the first stop (there is still a certain distance before departure). time), and when the vehicle arrives at the last station, there is also a phenomenon that the time for getting off the passenger is too long

Based on the above situation, if the effective passenger flow is calculated by using the actual departure time (TS) at the first stop of a bus - the actual arrival time (TE) at the last stop of a certain bus, the passenger flow of getting on the bus in advance and getting off the bus later will be omitted; if The earliest acquisition time of GPS using the bus (TS 0 )-GPS latest acquisition time (TE 0 ) to calculate the passenger flow, which will include more invalid passenger flow; otherwise, it is necessary to manually count the earliest passenger boarding time (TS') and the latest passenger disembarkation time (TE') of a certain flight. This method has a huge workload and requires Specialized personnel conduct statistics, so how to retain the passenger flow of early boarding at the first stop and delayed getting off at the last station while removing invalid passenger flow, and quickly determine the earliest passenger boarding time and the latest passenger disembarkation time are currently existing big problem

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