A Prediction Method of Passenger Flow in Urban Rail Transit Sections under Emergency Events

Abstract

The invention discloses a method for predicting urban rail transit section passenger flow under emergencies. The method includes: S1: Obtain urban rail transit physical network information, train operation information, passenger flow data and emergency event information, and determine the affected passenger flow; S2 : Update the affected passenger flow data based on the route selection behavior of passengers under emergencies; S3: Taking into account train capacity constraints and integrating unaffected passenger flow, calculate the passenger flow between each station of urban rail transit. This invention analyzes the travel behavior of passengers , accurately define the affected passenger flow during emergencies, study the passenger route selection behavior during emergencies based on the location of the affected passengers, determine the probability of selection of each alternative route, thereby accurately allocating the affected passenger flow, and at the same time The queuing model is established taking into account train capacity limitations, which improves the estimation accuracy of cross-section passenger flow under emergencies, and provides a basis for ensuring the stable operation of the urban rail transit system and the safe travel of passengers.

Description

technical field [0001] The invention relates to the technical field of urban rail transit. More specifically, it relates to a passenger flow prediction method for urban rail transit sections under emergencies. Background technique [0002] With the rapid development of social economy, urban rail transit has become an important way of transportation in big cities. However, while rail transit greatly facilitates people's daily travel, due to various factors such as equipment failure, operation management, and natural disasters, sudden interruptions of urban rail transit systems are inevitable. Emergencies often occur suddenly in the normal operation of rail transit, so emergencies often cause changes in the topology of rail transit networks. In this case, the traveler may abandon the originally planned route and reconsider the choice of the route according to the change of the travel cost. Under networked operating conditions, emergencies can spread widely and easily cause ...

AI Technical Summary

Problems solved by technology

On the one hand, there is no well-formed cross-section passenger flow forecasting method for emergencies. The existing methods are basically based on the distribution of cross-section passenger flow under normal conditions, and more often estimate the cross-section flow through the planned timetable. Consider the impact of changes in the actual train timetable and the planned timetable; on the other hand, it also fails to take into account the differences in the choices made by passengers at different locations under emergencies, and generally chooses the plan from the starting point, resulting in cross-sectional passenger flow. At the same time, there is a lack of analysis of passenger travel behavior under emergencies, and it is also impossible to accurately estimate the number of passengers who choose each plan under emergencies, thereby reducing the accuracy of cross-sectional passenger flows after emergencies.

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