Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands

By Wendy Bohte, K. Maat. 2009

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “…asked to both complete questionnaires and carry GPS loggers; and pure GPS-based surveys during which subjects are only asked to carry GPS loggers (Wolf et al., 2001; Axhausen et al., 2003; Hato et al., 2006; Stopher et al., 2008a, 2008b; Bohte and Maat, 2009; Chen et al., 2010; Gong et al., 2011).”
GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? (2013, background): “Still, such algorithms are unable to identify the exact activities practiced in many places (Bohte and Matt, 2009).”
Review of GPS Travel Survey and GPS Data-Processing Methods (2014, methodology): “Bohte and Maat (2009) also applied a rule-based system to detect trip purpose, mainly based on the GIS land-use data and the addresses of home and work place. They suggested that for a non-home or non-work location, if a trip ends within a radius of 50 metres from that location, it can be regarded a”
A GPS/GIS method for travel mode detection in New York City (2012, background): “Bohte and Maat (2009) achieved a 70% success rate in a mode detection effort in the Netherlands, with 75% for car mode, 35% for rail, 72% for bicycle, and 68% for walk.”