Supporting large-scale travel surveys with smartphones - A practical approach
Supporting large-scale travel surveys with smartphones - A practical approach
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DOI：
10.1016/j.trc.2013.11.005
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发表时间：
2014-06-01
影响因子：
8.3
通讯作者：
Maurer, Peter
中科院分区：
文献类型：
--
作者：
Nitsche, Philippe;Widhalm, Peter;Maurer, Peter
出行数据的收集是交通建模的关键任务。目前的数据收集基于昂贵且耗时的调查问卷，因此只能提供有限的横截面覆盖范围且更新不足。迫切需要有技术支持的旅行数据采集工具。我们提出了一种利用智能手机收集的数据支持旅行调查的新颖方法。携带电话的人的个人行程会自动重建，并且行程会被分为八种不同的交通方式之一。该任务由概率分类器集合与离散隐马尔可夫模型 (DHMM) 相结合来执行。分类基于从智能手机定位系统记录的运动轨迹和嵌入式加速度计信号中提取的特征。我们的方法可以通过包含从手机蜂窝网络获得的定位数据来应对 GPS 信号丢失，并且当无法以足够的精度重建轨迹时，仅依赖于加速度计功能。为了训练和评估模型，15 名志愿者在 2 个月内收集了奥地利维也纳大都市区 355 小时的探测旅行数据。区分八种不同的交通方式，分类结果范围从 65%（火车、地铁）到 95%（自行车）。智能手机的日益普及使得所提出的方法具有广泛使用的潜力，并且可以补充现有的旅行调查方法。 (C) 2013 Elsevier Ltd. 保留所有权利。
Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone's positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a wide-spread basis and can complement existing travel survey methods. (C) 2013 Elsevier Ltd. All rights reserved.