Developing and Validating a Statistical Model for Travel Mode Identification on Smartphones
Developing and Validating a Statistical Model for Travel Mode Identification on Smartphones
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DOI：
10.1109/tits.2016.2516252
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发表时间：
2016-02
影响因子：
8.5
通讯作者：
Behrang Assemi;Hamid Safi;M. Mesbah;L. Ferreira
中科院分区：
文献类型：
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作者：
Behrang Assemi;Hamid Safi;M. Mesbah;L. Ferreira
智能手机旅行调查能够捕捉个人旅行行为的准确细节。然而，提取所需的信息（例如，旅行模式和目的）是相对复杂的，特别是当依赖于智能电话的计算能力并且限制这些应用和第三方之间的通信时[例如，地理信息系统（GIS）。这些限制主要是为了通过智能手机自动识别旅行的模式和目的来实现被动数据收集。此外，应用程序和第三方之间有限的数据传输确保了调查参与者的隐私保护，并促进了大样本量的真实世界旅行调查。因此，本文的目的是开发一个模型的旅行模式识别，它可以集成到智能手机旅行调查，而不使用GIS数据或与参与者进行交互。大多数现有的模型和算法要么不准确，要么计算复杂，需要大量的处理能力。一项智能手机旅行调查，即智能手机高级旅行记录应用程序II（ATLAS II），被用来收集新西兰和澳大利亚昆士兰州的个人旅行数据。提出了一个详细的算法来清洗捕获的数据，分割成单一模式的行程，并开发多个统计模型进行比较，使用从新西兰收集的数据。首选的方法，这是适合与智能手机旅行调查应用程序的集成，验证使用两个独立的数据集从新西兰和澳大利亚。得到的模式识别模型（即，具有八个变量的嵌套Logit模型）可以在预处理之后以97%的准确度检测新西兰的旅行模式（即，数据清理和行程分割），澳大利亚为79.3%，无需任何预处理。
Smartphone travel surveys are able to capture accurate details about individuals' travel behavior. However, extracting the required information (e.g., travel mode and purpose) from the data captured by smartphone applications is relatively complex, particularly when relying on the computational power of smartphones and limiting the communications between these applications and third parties [e.g., geographic information systems (GIS)]. These limitations are mainly enforced to enable passive data collection through smartphones by automatically recognizing the mode and purpose of trips. Furthermore, limited data transfer between the application and third parties ensures the privacy protection of survey participants and facilitates real-world travel surveys with large sample sizes. Accordingly, the objective of this paper is to develop a model of travel mode identification, which can be integrated with smartphone travel surveys without using GIS data or interacting with participants. Most existing models and algorithms are either inaccurate or computationally complex, and require extensive processing power. A smartphone travel survey, namely, the Advanced Travel Logging Application for Smartphones II (ATLAS II), has been used to collect individuals' travel data across New Zealand and Queensland, Australia. A detailed algorithm is put forward to clean the captured data, segment trips into single modal trips, and develop multiple statistical models for comparison, using the data collected from New Zealand. The preferred approach, which is adapted for the integration with smartphone travel survey applications, is validated using the two separate data sets from New Zealand and Australia. The resulting mode identification model (i.e., a nested logit model with eight variables) could detect travel modes with the accuracy of 97% for New Zealand after preprocessing (i.e., data cleaning and trip segmentation) and 79.3% for Australia without any preprocessing.