Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area
Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area
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DOI：
10.1016/j.compenvurbsys.2015.04.001
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
T. K. Rasmussen;Jesper Bláfoss Ingvardson;Katrín Halldórsdóttir;O. A. Nielsen
中科院分区：
文献类型：
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作者：
T. K. Rasmussen;Jesper Bláfoss Ingvardson;Katrín Halldórsdóttir;O. A. Nielsen
全球定位系统数据收集已成为调查旅行行为的一个重要手段。这是因为，与传统的旅行调查方法相比，这些数据在理想情况下提供了更长时间内关于路线选择和旅行模式的详细信息。此外，与填写（大型）调查问卷相比，对受访者来说，佩戴GPS装置的要求更低。然而，它对数据的后处理提出了很高的要求。本研究开发并测试了一种结合模糊逻辑和基于GIS的算法来处理原始GPS数据。该算法被应用到收集在高度复杂的大规模多式联运网络的大哥本哈根地区的GPS数据。它检测行程，行程腿，并区分五种运输方式。该算法进行了验证，通过比较与控制问卷收集在相同的人和敏感性分析。这表明，该算法（i）为82%的报告行程确定了相应的行程，（ii）避免将非行程（如分散在活动周围）分类为行程，（iii）为90%以上的行程确定了正确的运输方式，以及（iv）对模型参数和阈值的规范具有鲁棒性。因此，该方法使得有可能在大规模多模式网络中使用全球定位系统进行旅行调查。
GPS data collection has become an important means of investigating travel behaviour. This is because such data ideally provide far more detailed information on route choice and travel patterns over a longer time period than possible from traditional travel survey methods. Wearing a GPS unit is furthermore less requiring for the respondents than filling out (large) questionnaires. It places however high requirements to the post-processing of the data. This study developed and tested a combined fuzzy logic and GIS-based algorithm to process raw GPS data. The algorithm is applied to GPS data collected in the highly complex large-scale multi-modal transport network of the Greater Copenhagen area. It detects trips, trip legs and distinguishes between five modes of transport. The algorithm was validated by comparing with a control questionnaire collected among the same persons and a sensitivity analysis was performed. This showed that the algorithm (i) identified corresponding trip legs for 82% of the reported trip legs, (ii) avoided classifying non-trips such as scatter around activities as trip legs, (iii) identified the correct mode of transport for more than 90% of trip legs, and (iv) were robust towards the specification of the model parameters and thresholds. The method thus makes it possible to use GPS for travel surveys in large-scale multi-modal networks.