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An Evaluation of the Performance of Smart Mobility Management to Tackle Traffic Congestion and Related Impacts Informal enquiries to: Neil Fergusion ([log in to unmask]) Department of Civil Engineering, Matthew Revie ([log in to unmask]), or John Quigley ([log in to unmask]), Department of Management Science Incidents on the road network, such as accidents and vehicle breakdowns, are a significant source of traffic congestion which produces negative economic, social and environmental impacts. The early detection and prediction of incidents are critical components of network management processes. Existing roadside detection systems are limited in their ability to predict and detect incidents. However, new opportunities to tackle this problem have arisen with the emergence of sensing, information and communication technologies which promise a far-reaching change in the data environment within the transport domain i.e. data collected from a combination of roadside, in-vehicle and nomadic devices. New techniques to analyse data in real-time from a variety of sources are also emerging. The purpose of this research project is to improve highway management by developing new methods of short-term incident prediction and detection which draw on historic and real-time data from multiple sources and evaluating the performance of these methods in different contexts. This project is jointly supervised by the Department of Civil and Environmental Engineering and the Department of Management Science. The project is also part funded by Cubic Transportation Systems – a leading worldwide provider of services and solutions in intelligent travel applications (http://cts.cubic.com/en-us/aboutus.aspx) – who will play an active role in the supervision of the project. There will be an opportunity to be seconded to Cubic for short periods of time to work on this project and also gain practical experience. The project will give the student the opportunity to work within an interdisciplinary and vibrant network of academics and access a range of training opportunities. The student will have the opportunity to influence the application area, although specific opportunities are likely to emerge from synergies with Strathclyde University’s Institute for Future Cities. Candidates should have a good Honours degree (minimum 2:1) and/or Master’s degree in a quantitative discipline such as engineering or statistics (amongst others). They may also have other relevant experience or skills which are relevant to this project. All applications must be accompanied by a cover letter indicating the candidate's relevant skills/experience and how they can contribute to this research, as well as a CV and relevant qualification transcripts. You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.