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Understanding travellers’ spatial preferences based on mobile internet usage data

Given a traveler’s favorite mobile internet content type, can we make a better guess on which type of places this traveler would visit? In this study, we used a special mobile phone dataset that includes not only the spatial-temporal traces but also the frequencies of the same users visiting each type of websites and apps, to identify the statistical relationships between one’s spatial preferences and mobile internet usage. [download link]

Road network design in a developing country using mobile phone data

Is there any correlation between telecommunication and travel demand? This study used mobile phone traces to depict mobility patterns, and related inter-regional travel demand with inter-regional telecommunication intensity. Based on that, a trip distribution model was developed and embedded in a road network optimization algorithm. The method was applied to a case study for road network design in Senegal. It is valuable and reproducible not only to Senegal but also to other countries where traditional mobility data is scarce but mobile phone data is available. [download link]

Using metro smart card data to model location choice of after-work activities

Given a metro commuter’s home and workplace stations, can we predict where this person would like to visit after work? To address this problem, a discrete choice model was estimated using smart card data from Shanghai, China. The model can further serve as a tool to help retail companies locate their business optimally and help urban decision makers plan transport networks and land use more reasonably. [download link]

publications

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.