基于Wi-Fi探针的车站通道走行时间实时估计与客流预警
王越1胡华2陈娟3
Station Passage Walking Time Real-time Estimation and Passenger Flow Early-warning Based on Wi-Fi Probes
WANG YueHU HuaCHEN Juan
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作者信息:1.苏交科集团股份有限公司， 210019， 南京
2.上海工程技术大学城市轨道交通学院， 201600， 上海
3.北京交通大学经济管理学院， 100044，北京
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Affiliation:JSTI Group, 210019, Nanjing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.07.008
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中图分类号/CLCN:U293.13
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栏目/Col:研究报告
摘要:
目的：为建立车站通道走行时间实时估计模型，提高城市轨道交通车站的运营效率与安全性，以提升乘客的出行体验。方法：采用基于Wi-Fi探针的数据采集方式，通过在车站通道中安装Wi-Fi探针收集信息数据，包括乘客设备MAC( 媒体接入控制)地址、信号强度、距离嗅探器的距离等信息。通过java语言对采集的数据进行初始处理，利用Mysql和navicat premium数据库的组合对数据进行深度清洗，验证了采用BPR美国联邦公路局函数建立车站通道走行时间实时估计模型的可行性，并采用蚁群聚类算法将延误时间作为对车站通道客流预警等级划分的依据。结果及结论：总结了Wi-Fi探针客流采集原理和原始数据清洗方法，建立基于BPR函数的车站通道走行时间实时估计模型，实现了车站通道乘客走行时间实时估计，其模型的准确率可达928%。将车站通道客流预警划分为畅通、基本畅通、拥挤、严重拥挤等4个等级，利用上海轨道交通11号线江苏路站的Wi-Fi探针数据进行了实例验证与分析，证明了模型的可适用性及预测精度。
Abstracts:
Objective: Model for station passage walking time real-time estimation is established to improve urban rail transit station operation efficiency and safety, to enhance the passenger travel experience.Method: By adopting a data collection method based on Wi-Fi probe, information data such as passenger device MAC (medium access control) address, signal strength, distance from sniffer are collected by Wi-Fi probes installed in station passage. The collected data are initially processed by java language, and further cleaned in-depth by using a combination of Mysql and navicat premium database, which verifies the feasibility of using BPR (bureau of public roads) function to establish a station passage walking time real-time estimation model, and the ant colony clustering algorithm is used to classify the station passage passenger flow early-warning level based on delay time.Result & Conclusion: The Wi-Fi probe passenger flow collection principle and original data cleaning method are summarized, station passage walking time real-time estimation model based on BPR function is established, and real-time estimation of passenger walking time through station passage is realized, the model for which reaches an accuracy rate of 92.8%. The passenger flow early-warning is classified into four levels: smooth, generally smooth, crowded and severely crowded. The model applicability and prediction accuracy is proved by the practical case verification and analysis of Shanghai Rail Transit Line 11 Jiangsu Road Station Wi-Fi probe data.
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