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首页> 外文期刊>Computers，environment and urban systems >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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Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area

机译：使用可穿戴GPS设备从旅行调查中扣除行程腿和行车方式的改进方法：以大哥本哈根地区为例

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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. (C) 2015 Elsevier Ltd. All rights reserved.
机译：GPS数据收集已成为调查旅行行为的重要手段。这是因为与传统旅行调查方法相比，此类数据理想地在更长的时间段内提供有关路线选择和旅行模式的详细得多的信息。此外，与填写（大型）调查表相比，佩戴GPS装置对受访者的要求更低。但是，它对数据的后处理提出了很高的要求。这项研究开发并测试了结合模糊逻辑和基于GIS的算法来处理原始GPS数据。该算法适用于大哥本哈根地区高度复杂的大规模多式联运网络中收集的GPS数据。它可以检测行程，行程行程并区分五种运输方式。通过与在同一人中收集的对照调查表进行比较来验证该算法，并进行了敏感性分析。这表明算法（i）为报告的82％的出行腿确定了相应的出行腿，（ii）避免将非出行（例如活动周围的散布）归类为出行腿，（iii）确定了超过90％的行程分支，以及（iv）对模型参数和阈值的规范具有鲁棒性。因此，该方法使得可以将GPS用于大规模多模式网络中的旅行调查。 （C）2015 Elsevier Ltd.保留所有权利。

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