Car-level congestion and position estimation for railway trips using mobile phones

Yuki Maekawa, A. Uchiyama, H. Yamaguchi, T. Higashino
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引用次数: 22

Abstract

We propose a method to estimate car-level train congestion using Bluetooth RSSI observed by passengers' mobile phones. Our approach employs a two-stage algorithm where car-level location of passengers is estimated to infer car-level train congestion. We have learned Bluetooth signals attenuate due to passengers' bodies, distance and doors between cars through the analysis of over 50,000 Bluetooth real samples. Based on this prior knowledge, our algorithm is designed as a Bayesian-based likelihood estimator, and is robust to the change of both passengers and congestion at stations. The car-level positions are useful for passengers' personal navigation inside stations and car-level train congestion information helps determine better strategies of taking trains. Through a field experiment, we have confirmed the algorithm can estimate the location of 16 passengers with 83% accuracy and also estimate train congestion with 0.82 F-measure value in average.
利用移动电话进行铁路出行的车级拥堵和位置估计
我们提出了一种利用乘客手机观察到的蓝牙RSSI来估计车厢级列车拥堵的方法。我们的方法采用两阶段算法,其中估计乘客的车级位置来推断车级列车拥堵。通过对5万多个蓝牙真实样本的分析,我们了解到蓝牙信号会因乘客的身体、车与车之间的距离、车门等因素而衰减。基于这种先验知识,我们的算法被设计为基于贝叶斯的似然估计,并且对车站乘客和拥堵的变化都具有鲁棒性。车厢级别的位置对乘客在车站内的个人导航很有用,车厢级别的列车拥堵信息有助于确定更好的乘坐火车的策略。通过现场实验,我们证实了该算法能以83%的准确率估计出16名乘客的位置,并能以平均0.82的F-measure值估计列车拥堵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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