Car-level congestion and position estimation for railway trips using mobile phones
Car-level congestion and position estimation for railway trips using mobile phones
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DOI：
10.1145/2632048.2636062
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Yuki Maekawa;Akira Uchiyama;Hirozumi Yamaguchi;T. Higashino
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文献类型：
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作者：
Yuki Maekawa;Akira Uchiyama;Hirozumi Yamaguchi;T. Higashino
我们提出了一种利用乘客手机观测到的蓝牙 RSSI 来估计车厢级列车拥堵情况的方法。我们的方法采用两阶段算法，通过估计车厢级别的乘客位置来推断车厢级别的列车拥堵情况。通过对超过50,000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、距离和车与车之间的门而衰减。基于这些先验知识，我们的算法被设计为基于贝叶斯的似然估计器，并且对于乘客和车站拥堵的变化具有鲁棒性。车厢级位置有助于乘客在站内进行个人导航，车厢级列车拥堵信息有助于确定更好的乘车策略。通过现场实验，我们证实该算法能够以 83% 的准确率估计 16 名乘客的位置，并以平均 0.82 F-measure 值估计列车拥堵情况。
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.