Abstract
Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning). This paper therefore, attempts to fully understand these aspects in the process of inference. We aim to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The framework was tested using coarse-grained GPS data, which has been avoided in previous studies, achieving a promising accuracy of 88% with a Kappa statistic reflecting almost perfect agreement.
Highlights
Understanding travel behaviour is important for many applications such as studying tourist activity (Edwards, Griffin, Hayllar, Dickson, & Schweinsberg, 2009) or the impact of a strike on transportation systems (Tsapakis et al, in press)
Among these practices are GPS-based travel surveys, where participants carry a GPS device for a certain duration of time and following this up by a prompt recall survey to report trip information, such as the transportation modes they used in every trip (Stopher, 2008)
The framework we propose in this work is based on Support Vector Machines (SVMs) to classify GPS segments into respective transportation modes
Summary
Understanding travel behaviour is important for many applications such as studying tourist activity (Edwards, Griffin, Hayllar, Dickson, & Schweinsberg, 2009) or the impact of a strike on transportation systems (Tsapakis et al, in press). Some standard data collection practices have been in place in order to collect travel data Among these practices are GPS-based travel surveys, where participants carry a GPS device for a certain duration of time and following this up by a prompt recall survey to report trip information, such as the transportation modes they used in every trip (e.g. cycle, walk, bus and so forth) (Stopher, 2008). Research has emerged in the previous decade attempting to infer the transportation mode from GPS data. This inference could largely replace or complete a lot of the feedback required by users when labelling and tagging travel diaries.
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