Real-time bus passenger analysis system and construction method thereof

Abstract

The invention discloses a construction method of a real-time bus passenger analysis system. The construction method includes the steps: performing pretreatment analysis for historical bus data according to data processing rules and historical data analysis of bi-directional bus card swiping passengers based on a Spark cloud computing platform; acquiring getting off behaviors of various stations of the passengers by the aid of historical data of bi-directional getting on behaviors of the passengers, and constructing getting on and getting off station distribution of bus passengers of routes by a statistic learning method; acquiring a data analysis computing method of passenger density of intervals by the aid of the getting on behaviors of the passengers; pre-processing bus data in real time by the aid of Spark, and dynamically generating bus station lists by the aid of bus stop records; feeding back background data processing results to foregrounds in real time by the aid of Spark Streaming technology, and reading and writing dynamic bus data at high speed by the aid of Redis; and analyzing the bus data in real time, and computing passenger density according to analysis results of the historical data.

Description

Technical field [0001] The invention relates to the field of traffic data analysis, in particular to the use of a large amount of historical data and a cloud platform to dynamically and real-timely perform efficient analysis and data visualization of continuously updated real-time bus swiping data. Background technique [0002] In recent years, the pressure on urban traffic has increased rapidly, putting tremendous pressure on urban economic development, talent introduction, and sustainable environmental development. Therefore, prioritizing the development of urban public transport and improving passengers’ satisfaction with the public transport system have become current research hotspots at home and abroad. [0003] Intelligent public transportation is an important subsystem of Intelligent Transportation System (ITS). The system aims to use intelligent dispatching system, automatic counting system, passenger information system, automatic vehicle positioning system, etc. to fully...

AI Technical Summary

Problems solved by technology

[0004] Due to the lack of high-performance platform support, many systems related to dispatching and bus information integration and analysis are stuck in the analysis of a single bus and a single time period, and cannot provide high-reliability analysis for all bus systems and comprehensive passenger facts. information feedback

At the same time, due to the lack of effective analysis of a large amount of historical bus data, the analysis results lack credibility and have disadvantages such as poor accessibility and high time-consuming

[0005] Traditional data mining algorithms based on machine learning are affected by problems such as coupling between eigenvalues, information dynamics, and large temporal and spatial correlations, making the structural design of machine learning algorithms difficult.

At the same time, because the analysis of bus data involves professional knowledge such as bus dispatching evaluation, bus system management, and bus dispatching system planning, it is difficult to use traditional machine learning methods to analyze bus data.

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