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Detecting Swiching Points and Mode of Transport from GPS Tracks
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes.

This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset. The available GPS dataset is collected for travel modes of walk, bike, car, and public transport travel modes such as bus, train and subway.

The developed procedure consists of two stages where the first is the dividing the track trips into trips and further the trips into segments by means of a segmentation process. The segmentation process is based on an assumption that a traveler switches from one transportation mode to the other. Thus, the trips are divided into walking and non walking segments.

The second phase comprises a procedure to develop a classification model to infer the separated segments with travel modes of walk, bike, bus, car, train and subway. In order to develop the classification model, a supervised classification method has been used where decision tree algorithm is adopted.

The highest obtained prediction accuracy of the classification system is walk travel mode with 75.86%. In addition, the travel modes of bike and bus have shown the lowest prediction accuracy. Moreover, the developed system has showed remarkable results that could be used as baseline for further similar researches.

Place, publisher, year, edition, pages
2012. , 51 p.
Keyword [en]
Travel demand model, Supervised classification model, Decision tree, Data mining, Artificial intelligence, Inferring travel modes, GPS in travel survey
National Category
Transport Systems and Logistics
URN: urn:nbn:se:liu:diva-91320ISRN: LiU-ITN-TEK-A--12/069--SEOAI: diva2:617172
Subject / course
Transportation Systems Engineering
Available from: 2013-04-22 Created: 2013-04-22 Last updated: 2013-04-22Bibliographically approved

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