A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures
A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures
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DOI：
10.1016/j.trc.2014.03.015
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发表时间：
2014-07-01
影响因子：
8.3
通讯作者：
Bierlaire, Michel
中科院分区：
文献类型：
--
作者：
Danalet, Antonin;Farooq, Bilal;Bierlaire, Michel
在本文中，我们提出了一种利用通信网络基础设施，特别是WiFi痕迹来检测行人访问的活动场景序列的方法。针对WiFi定位质量较差的问题，提出了一种基于WiFi轨迹推断活动场景位置的概率方法，并在考虑先验知识的基础上计算行人网络中观察到这些轨迹的可能性。该方法的输出由与为真的可能性相关的活动-情节序列的候选者组成。该方法在已知活动序列生成的跟踪上进行验证，同时在一组匿名用户上进行性能评估。结果表明，通过合并地图上活动位置的信息、WiFi测量数据和有关行人基础设施中的时间表和吸引力的先验信息，可以预测活动场景的数量和活动场景的位置和持续时间。对序列中每个活动片段的歧义进行了显式测量。(C)2014爱思唯尔有限公司。保留所有权利。
In this paper, we propose a methodology to use the communication network infrastructure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episode locations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists of candidates of activity-episodes sequences associated with the likelihood to be the true one. The methodology is validated on traces generated by a known sequence of activities, while the performance being evaluated on a set of anonymous users. Results show that it is possible to predict the number of episodes and the activity-episodes locations and durations, by merging information about the activity locations on the map, WiFi measurements and prior information about schedules and the attractivity in pedestrian infrastructure. The ambiguity of each activity episode in the sequence is explicitly measured. (C) 2014 Elsevier Ltd. All rights reserved.