A time-varying user equilibrium dynamic network evolution passenger flow prediction system and method

Abstract

The invention discloses a system and method for predicating time-varying user dynamic equilibrium network-evolved passenger flow and belongs to the technical field of urban rail traffic safety. The system comprises an AFC (auto fare collection) system, and a video terminal and the like. A network database is sequentially connected with a passenger flow distribution module, a passenger flow correction module and a passenger flow analysis module. The passenger flow video analysis module is connected with the network database module and the passenger flow correction module respectively. The video terminal and the AFC system transmit passenger information data and store the same in a network database. The passenger flow video analysis module analyzes real-time video data, and the passenger flow correction module adopts an AUKF (adaptive unscented Kalman filter) for preprocessing. The passenger flow data are matched and predicated by means of a passenger flow prediction algorithm, and services like an inquiry are provided by a human-computer interaction terminal. The requirements of multiple users for road network short-term prediction under emergency conditions are met, real-time distribution and dynamic prediction for the bounded rationality of the passenger flow are realized, and real-time inquiring, sharing and decision making of enterprises for passenger flow information are met.

Description

technical field [0001] The invention relates to a time-varying user balance dynamic network evolution passenger flow prediction system and method, belonging to the technical field of urban rail transit safety. Background technique [0002] With the growth of my country's urban rail transit construction, the importance of its safe operation and emergency dispatch has become increasingly prominent. Especially in the face of unconventional emergencies, there is still a lack of a method that can quickly and effectively simulate and predict the passenger flow distribution of urban rail transit under emergencies in real time, so as to ensure that the operation decision-making department makes correct decisions . [0003] The origin and destination (OD) distribution of the existing rail transit network is mainly based on offline data for normal network estimation, while in emergencies, OD estimation must be estimated online with the support of real-time data, and at the same time,...

AI Technical Summary

Problems solved by technology

[0004] Under emergencies, due to changes in network connectivity and congestion characteristics, passengers may even have to change their daily balanced route decisions, resulting in an unbalanced and time-varying passenger flow on the rail transit network. Existing systems and methods are difficult to deal with emergencies. Accurately estimate the dynamic network state under

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