Internet-of-things-based passenger flow analysis and prediction system of urban railway system

Abstract

The invention provides an internet-of-things-based passenger flow analysis and prediction system of an urban railway system. The system is composed of traffic passenger flow collectors and a traffic passenger flow analysis and prediction center. The traffic passenger flow collectors consist of wireless communication modules, data collectors, and micro processors and power supplies. The traffic passenger flow analysis and prediction center includes a wireless repeater, a statistical analyzer, a passenger flow predictor and a driving planner. According to the internet-of-things-based passenger flow analysis and prediction system, the structure is simple, intelligent and humanized; and the passenger flow features can be analyzed deeply based on the operation data. Therefore, the operation department can master the network passenger flow rule well to carry out driving planning conveniently; the network operation efficiency is improved; and the periodic passenger flow congestion occurrenceis reduced.

Description

technical field [0001] The invention relates to a rail transit passenger flow analysis and prediction system, in particular to an urban rail transit passenger flow analysis and prediction system based on the Internet of Things. Background technique [0002] With the rapid development of urban modernization, urban rail transit is gradually entering the era of networked operation. While greatly improving transportation capacity and travel convenience, it also brings new problems and challenges to operation scheduling. Under the conditions of networked operations and "seamless transfers", passenger travel paths become more diverse, and the uneven distribution of passenger flow in time and space becomes more prominent. On the one hand, this will cause periodic passenger flow congestion and increase operational safety risks; on the other hand, it will also cause the inability to fully utilize rail transit capacity and reduce the efficiency of network operations. Therefore, an i...

AI Technical Summary

Problems solved by technology

[0003] At present, urban rail transit operators generally use traditional database systems for business and transaction processing, which can only provide simple statistics of passenger flow data and indicators, and lack complex analysis functions such as passenger flow trends and passenger flow forecasts

At the same time, there is a serious shortage of urban rail transit passenger flow characteristics analysis and passenger flow forecasting. Relying on manual statistics and questionnaires cannot accurately count passenger flow data, and cannot provide real-time and reliable decision support for the operation of urban rail transit systems.

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