Predicting Tour Patterns Derived from Ubiquitous Data Sources

Abstract

Ubiquitous data sources (e.g. GPS traces, mobile phone call records, smart card entries etc.) are increasingly being popular among researchers for deriving mobility patterns. These mobility patterns depend on socio-economic characteristics of the traveler as well as associated situational, contextual and environmental factors. These patterns therefore vary substantially with age, gender, employment status, income level and other demographic factors. Habitual markers (propensity to use the internet, social networking websites, mobile phones or media for example) also provide useful indications about these patterns. In this research, the day-to-day tour patterns of travelers have been extracted from GPS and WLAN records and a discrete choice modeling framework has been proposed to predict these patterns using demographic factors, habitual markers and other travel related attributes collected from travelers of Lausanne, Switzerland. The model parameters are estimated by maximum likelihood technique using the software BIOGEME. Estimated model parameters confirm that demographic factors (e.g. age, occupation and gender) combined with habitual markers (e.g. habit of listening to music on smart phone) and other contextual attributes (e.g. day of the week) can be used to predict the tour pattern of an individual on a certain day. The developed model demonstrates how information obtained from ubiquitous data sources can be successfully used as a tool for transportation planning and management.

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