Method for forecasting section passenger volume of urban rail transit under emergencies

Abstract

The invention discloses a method for forecasting the section passenger volume of urban rail transit under emergencies. The method comprises steps of S1: acquiring urban rail transit physical network information, train running information, passenger volume data and emergency information to determine an affected passenger volume; S2: updating affected passenger volume data according to route selection behaviors of passengers under emergencies; and S3: taking into account the constraints of train capacity, integrating the non-affected passenger volume, and calculating the section passenger volume between stations of urban rail transit. By analyzing the passengers' travel behavior and accurately defining the affected passenger volume during the emergency, the method studies the passenger route selection behaviors under emergencies according to the locations of the affected passengers and determines the probability of being selected of each alternative route so as to accurately distribute the affected passenger volume. Meanwhile, the method takes into account the train capacity constraints to establish a queue model so as to improve the estimated accuracy of section passenger volume under emergencies, and to provide a basis for 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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