Using wearable GPS devices in travel surveys

GPS data collection has become an important means of investigating travel behaviour, as it ideally provides far more detailed information than traditional travel survey methods. While setting fewer requirements to the respondents, it however sets high requirements to the post processing of the data collected. This study proposes 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 Greater Copenhagen Area network and detects trip legs and distinguishes between five modes of transport. The algorithm shows promising results by (i) identifying trip legs for 82% of the reported trip legs, (ii) not classifying non‐trips such as scatter around activities as trip legs and (iii) identifying the correct mode of transport in more than 90% of trip legs for which corresponding observed modes are available.