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The University of Southampton
Web Science Institute

Intelligence Mobile Blockchain-based Fraudulent and Risky Behaviour Prediction for Personal Financial Applications


The EU Transport White Paper (2011) sets out a vision for our cities to be carbon free by 2030. However, freight distribution continues to generate significant traffic, congestion, noise and pollution within cities. Urban areas see a number of couriers, each making hundreds of parcel deliveries every day. The planning of deliveries is often done manually by individual drivers, yielding inefficient schedules, particularly for drivers unfamiliar with the environment, resulting in half-empty vans, increased amount of driving, and traffic congestion by the constant use of the limited parking spaces. The sheer complexity of such day-to-day parcel delivery operations requires a socio-technological solution that necessitates the development and use of an automated decision support tool to enable drivers to optimise their delivery schedules, and also allows interaction with and between users (i.e., planners and drivers) to enable knowledge exchange through collaborative planning in real-time. Current logistics tools are static in the way they work and do not allow for such collaborative human-computer interaction. The main goal here would be to produce a prototype tool, developed with active involvement by several parcel couriers operating in London, who would provide the real-data needed for testing. The tool we envisage would be accessible to all users remotely and would operate in real time to react to any changes. This makes the web the ideal platform to run it on (e.g., a mobile application). We would expect significant benefits from the use of the tool, including reduction in delivery times and traffic, as well as reduced energy and fuel requirements. The proposal draws on interdisciplinary expertise, bringing together transportation research (FEE), optimisation methods (FBLA) and web science.


Principal Investigator: Dr Tiejun Ma, Southampton Business School

Co-Investigator: Dr David Stillwell, University of Cambridge

Co-Investigator: Dr. Xiaoli Chen, Moore Stephen Ltd

Co-Investigator: Professor Xiao Tian Zhu, HSBC Business School, Beijing University

Final Report

View report here.

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