Travel mode detection based on GPS track data and Bayesian networks

Over the past couple of decades, there has been an exponential increase in the collection of large-scale GPS data from household/personal travel surveys all over the world. A range of algorithms, which vary from specific rules to advanced machine learning methods, have been applied to extract travel modes from raw GPS data collected by smartphone-based travel surveys. However, most of the methods applied neither describe the interaction between features influencing the travel mode decision nor effectively deal with the ambiguity inherently incorporated in these features. This paper identifies travel modes with a Bayesian network, whose structure is established based on a K2 algorithm and corresponding conditional probability tables are estimated with maximum likelihood methods. Five representative travel modes - walk, bike, e-bike, bus and car - are distinguished using the resulting Bayesian network. Additionally, the low speed rate and the average heading change are introduced to reduce uncertainties between bike and e-bike segments and between bus and car segments. The derived travel modes are then compared with those retrieved in the prompted recall survey by telephones. Consequently, more than 86% of segments have the travel mode correctly identified for each travel mode, with over 97% of walk segments being properly flagged. Results from the study demonstrate that GPS travel surveys provide an opportunity to supplement traditional travel surveys.