Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics
Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics
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DOI：
10.1016/j.trc.2017.05.003
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发表时间：
2017-07-01
影响因子：
8.3
通讯作者：
Haworth, James
中科院分区：
文献类型：
--
作者：
Bantis, Thanos;Haworth, James
随着地理移动电话应用程序的日益普及，研究人员可以以相对较高的空间和时间分辨率收集移动数据。然而，此类数据缺乏语义信息，例如个人与可用交通方式的交互。另一方面，传统的流动性调查提供了社会人口特征与交通方式选择之间关系的详细快照。目前，交通模式检测是使用速度、加速度和方向等特征单独使用或与 GIS 数据结合使用。将这些信息与旅行者的社会人口特征相结合，有可能提供更丰富的建模框架，可以利用年龄和残疾等变量促进更好的交通方式检测。在本文中，我们探索了在交通方式检测任务中纳入环境因素和旅行者个人特征的可能性。使用动态贝叶斯网络，我们通过使用传统流动性调查数据构建的信息丰富的狄利克雷先验，对转换矩阵进行建模，以解释此类辅助数据。结果表明，即使在移动数据稀疏的情况下，也可以在拥有丰富的建模框架的同时，与最广泛使用的分类算法实现相当的精度。 (C) 2017 Elsevier Ltd. 保留所有权利。
With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic, information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of, travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data. (C) 2017 Elsevier Ltd. All rights reserved.