School of Architecture, Building and Civil Engineering

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Professor Mohammed A Quddus PhD, MEng (Civil), BSc (Civil Eng

Photo of Professor Mohammed A Quddus

Professor of Intelligent Transport Systems

Part B Year Tutor: Air Transport Management, Transport and Business Management

Dr Quddus obtained a PhD from Imperial College London in 2005 where he was working as a research assistant for five years on a number of research projects. He received an MEng degree in Civil Engineering from the National University of Singapore in 2001 and a BSc degree in Civil Engineering from BUET (Bangladesh University of Engineering and Technology) in 1998. He joined Loughborough University as a Lecturer in Transport Studies in 2006,  promoted to Senior Lecturer in 2010 and has recently been awarded a personal Chair in "Intelligent Transport Systems" in August 2013.

Broad interests and expertise

  • Geographic Information Science (GIScience)
  • Intelligent Transport Systems (ITS)
  • Transport Risk and Safety
  • Classical and Bayesian Statistical Modelling of Transport Data
  • Energy Demand Modelling

Professional affiliations 

  • Fellow of Higher Education Academy
  • Committee Member of Geographic Information Science and Applications (ABJ60) - Transportation Research Board (Washington D.C., USA)
  • Executive Committe Member (2008 - 2011) - Universities' Transport Studies Group (UTSG), UK

 

 

Research interests

  • Crash Mapping and Modelling
  • Map Matching Integrity for Autonomous Vehicles
  • Computational Intelligence Techniques
  • Statistical Modelling of Transport Data
  • Spatial Econometrics using Bayesian Inferences and GIS
  • End-user Transport Energy Demand Modelling 

Research project

  • Intelligent algorithms for crash mapping

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to accurately carry out some key analyses in accident research including the identification of hazardous road segments, segment-based risk mapping and accident risk modellingExisting accident mapping algorithms have some shortcomings that include: (i) they are not easily ‘transferable’ as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent to the road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables found in most accident databases (e.g. road name and type, direction of vehicle movement and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and scale of inaccuracies, and (iii) develop a general algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segments on which accidents were occurred. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments,  an  Artificial Neural Network (ANN) approach using the single-layer perceptron is used to assist “learn” the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other  commonly used methods.

Methods: Fuzzy Logic, Genetic Algorithm, Statistical Models, Artificial Neural Network

  • Associate Editor - Journal of Intelligent Transportation Systems: Technology, Panning and Operations
  • Associate Editor - International Journal of Vehicle Information and Communication Systems
  • Guest Editor - Journal of ITS: Technology, Planning and Operations
  • Member of Editorial Boards - Accident Analysis and Prevention; Analytic Methods in Accident Research
  • Paper reviews for: IEEE Transactions on Intelligent Transportation Systems, Transportation Research A: Policy and Practice, Transportation Research C: Emerging Technologies, Transportation Research D, Transportation Research Record, International Journal of Geographic Information Science, Accident Analysis and Prevention