A Dynamic Estimation Method of Passenger Flow od in Urban Rail Transit Network

Abstract

The invention discloses a method for dynamic passenger flow OD estimation of an urban mass transit network. The method comprises: on the basis of historical passenger flow data, with time interval set, a one-way-ticket passenger flow OD matrix with poor passenger flow spatial-temporal distribution stability is improved by using a moving average method and an improved passenger flow OD distribution matrix is generated; a passenger flow diversion matrix is calculated; a dynamic flow relationship between an OD flow and a passenger flow going in or out of a station is constructed by combining a travel time distribution rule of the OD flow; according to the dynamic flow relationship and an in-out passenger flow information uploaded in real time, an OD dynamic estimation state space model is established; the model is solved by using a Kalman filter method; an OD estimation result is corrected by using a standardization method and an optimal estimation value is obtained; and effectiveness of the method is also checked. According to the method disclosed by the invention, statistics is carried out based on in-out transaction data uploaded in real time and historical passenger flow data and a Kalman-filter-based state space model is established, so that real-time passenger flow demand distribution structure can be estimated and a data support can be provided for dynamic passenger flow management of the rail transit enterprise.

Description

technical field [0001] The invention relates to a method for dynamically estimating OD of passenger flow in urban rail transit lines and networks. Background technique [0002] With the rapid development of urban rail transit, large and medium-sized cities have gradually stepped into network operation. The complexity of the line network structure leads to the strengthening of the randomness of passenger travel behavior, showing dynamic and complex characteristics in the distribution structure of passenger flow demand, which brings great challenges to the transportation organization of rail transit. It is urgent to use an appropriate demand estimation model to To study the temporal and spatial distribution of passenger flow demand in a short time frame, in order to improve the dynamic operation management level and system adaptability of rail transit. [0003] Since the 1980s, domestic and foreign scholars have carried out a lot of research on OD dynamic estimation and forme...

AI Technical Summary

Problems solved by technology

However, the existing OD estimation methods in the application of rail transit mainly have the following shortcomings: ① Existing research mainly focuses on the field of road traffic, without considering the rail transit network structure and passenger flow characteristics. On the one hand, there are shortcomings of low precision and low calculation efficiency; ②The flow relationship between OD flow and collected information flow in existing models is mostly based on the easy acquisition of section flow, but the real-time section passenger flow information in rail transit is not It is difficult to obtain, and only the information of passenger flow in and out of the station can be obtained, so the dynamic flow equation based on the cross-section collection flow is difficult to apply to the rail transit network

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