Urban railway traffic random passenger flow loading method

Abstract

The invention discloses an urban railway traffic random passenger flow loading method which comprises the following steps: 1.1, initializing, dividing one day into a plurality of time windows averagely, loading basic data, at the same time, setting the total number of the time windows as n, setting and marking an initial value of a variable t of the time windows as 1, and setting an initial value CN1 of road congestion charge as 0; 1.2, reading an origin and destination (OD) distribution volume of a tth time window, based on a basically effective path collection, calculating time of a first regular bus and a last regular bus of each path, judging the relationship of the time and the tth time window, and generating a dynamic effective path collection; 1.3, based on the dynamic effective path collection and an urban railway traffic passenger going-out path selection model, confirming a selected portion of each effective path between the OD, and obtaining a path flow; 1.4, performing statistics on a passenger flow of each section of a road, and updating the road congestion charge CNt+1; and 1.5, stopping the judgment, if the t is less than n, t=t+1 and continuing to iterate from the step 1.2, or, stopping the method.

Description

technical field [0001] The invention relates to the technical field of rail transit, in particular to a random passenger flow loading method for urban rail transit. Background technique [0002] Random passenger flow loading of urban rail transit network is the final link of passenger flow demand forecasting in urban rail transit. Passenger flow loading can not only obtain important passenger flow indicators such as cross-section flow, transfer volume and line flow to guide train capacity allocation and train operation planning, but also The freight clearing table of different operating entities can be obtained, so efficient and accurate passenger flow loading has important practical significance. [0003] The current random passenger flow loading methods for urban rail transit networks are divided into loading based on aggregate theory and loading based on non-aggregate theory (Logit or Probit model). The results of stochastic passenger flow loading based on aggregate theo...

AI Technical Summary

Problems solved by technology

However, the current random passenger flow loading of urban rail transit network based on non-aggregation theory lacks consideration of the impact of road network topology and path direction on passenger route selection; generally, the congestion charge is expressed by a piecewise function, which cannot reflect the congestion charge's preference for passenger route selection The continuous change of the impact, and some of the parameters are generally derived from experience values; the impact of the first and last train restrictions on the passenger flow loading results is generally ignored

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