基于物联网设备检测数据的城市轨道交通车站站台多维度实时客流密集度指数预测方法研究

Real-time Forecast of Passenger Crowd Index on Urban Rail Transit Station Platform Based on Test Data of Internet of Things

实时准确的客流预测是城市轨道交通客流预警和疏导的基础。本文针对城市轨道交通车站站台的实时客流密集度指数预测问题,根据实时客流的"间歇性"特点,依据30s为周期的真实检测数据,分别构建低、中、高3个时间维度在线实时预测模型。根据应用需要,对30s低纬度预测采用自回归与移动平均整合模型(ARIMA),对3min左右中维度提出多因素logistic预测模型,15min构建一种灰色与移动平均整合模型,并分别对预测参数进行估计。通过对10余个车站早晚高峰及平峰不同数据的大量在线实验验证模型的准确性,以北京地铁动物园站为例进行介绍,3个维度精度分别达到97%、95%、99%。结果表明:采用本文提出的模型较其他时间序列模型进行城市轨道交通车站设施的实时客流预测,具有更好的预测性能。本文所提模型已经用于北京市轨道交通安全防范物联网示范工程中,初步取得较好的实践效果。

Accurate real-time forecast of passenger flow is the basis of passenger flow early warning and evacua-tion in urban rail transit.This article focused on the real-time station platform passenger crowd index on fore-cast problem present with urban rail transit.According to the intermittent feature of real-time passenger flow, the on-line real-time forecast models for 30 s,3 min and 15 min dimensions were built on the basis of 30 s cyc-ling of real test data.In view of practical needs,the ARIMA model for 30 s,the multi factor logistic model for 3 min and the integrated gray and move average model for 1 5 min were built to estimate forecast parameters re-spectively.On-line tests during morning and evening peak time and common time at more than 10 stations proved the correctness of the structrual models.Taking the Beij ing Metro Zoo Station for case study,the de-grees of accuracy corresponding to the three above-mentioned time dimensions were obtained as 9 7%,9 5% and 9 9%.The research results show that the proposed models are of good effects on predicting real-time passenger flow in urban rail transit stations.The models have been used in the demonstrative project of the Beijing Metro internet of things for safety and initial results have been achieved.