On Analysing Urban Mobility Data for Smart Cities

  • On Analysing Urban Mobility Data for Smart Cities

On Analysing Urban Mobility Data for Smart Cities

Principal Supervisor: Dr Cheng Long
Second Supervisor: Dr Deepak Padmanabhan

+ Project Description

It is expected that by 2050, more than 2.5 billion people would reside in cities . With the proliferation of location sensing devices, the generation of urban mobility data (e.g., check-ins, geo-tagged photos, pick-up and drop-off locations, points-of-interests, etc.) has exploded. This project is to develop models, algorithms and systems for collecting and integrating urban mobility data from multiple sources (e.g., record linkage, public APIs) and analyzing the data for making our cities better places to live. For example, an interesting problem for exploration is to develop techniques for eco-routing, which is an emerging research problem recently due to its significant potential of energy saving (e.g., UPS identified that avoiding making left-hand turns helps to save 98 million minutes of idle time per year ). The idea is to compute paths which save the fuel consumption the most (based on the slope data, friction data of road segments and also engine measurement data of vehicles) instead of those of the smallest travel time.

+ How to Apply

Applicants should apply electronically through the Queen’s online application portal at:

+ Contact Details

Supervisor Name: Dr. Cheng Long

Computer Science Building,
18 Malone Road,
BT9 5BN             



+44 (0)28 9097 4783