UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification

Bolbol, A; Cheng, T; Tsapakis, I; Haworth, J; (2012) Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems , 36 (6) 526 - 537. 10.1016/j.compenvurbsys.2012.06.001. Green and gold open access

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1699Kb

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.

Type:Article
Title:Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification
Open access status:An open access publication. A version is also available from UCL Discovery.
DOI:10.1016/j.compenvurbsys.2012.06.001
Publisher version:http://dx.doi.org/10.1016/j.compenvurbsys.2012.06.001
Language:English
Additional information:This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords:Transportation mode, Classification, Variable selection, Svm, Travel behaviour
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering

View download statistics for this item

Archive Staff Only: edit this record