Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 222222
Loughborough University

Civil and Building Engineering

School staff

Quddus, Mohammed A

Professor Mohammed A Quddus

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Dr Quddus obtained a PhD from Imperial College London in 2006 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 was awarded a personal Chair in "Intelligent Transport Systems" in August 2013.

Professional Affiliations

  • Fellow of UK Higher Education Academy
  • Committee Member of Safety Data, Analysis and Evaluation - Transportation Research Board (Washington D.C., USA)
  • Executive Committe Member (2008 - 2011) - Universities' Transport Studies Group (UTSG), UK

External Activities

  • Associate Editor - International Journal of Vehicle Information and Communication Systems
  • Associate Editor - Journal of Intelligent Transportation Systems: Technology, Panning and Operations (2010 - 2013)
  • 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

Broad Interests and Expertise

  • Geographic Information Science (GIScience)
  • Intelligent Transport Systems (ITS)
  • Transport Risk and Safety
  • Connected and Autonomous Vehicles (CAVs)
  • Classical and Bayesian Statistical Modelling of Transport Data

Research Interests

  • Map-Matching and Pattern-Matching Algorithms
  • Connected and Autonomous Vehicles (CAVs): safety, operational and planning aspects
  • Statistical Modelling of Transport Data
  • Spatial Econometrics using Bayesian Inferences and GIS
  • Naturalistic Driving Studies (NDS)

Previous Research Projects

Real-time map-matching algorithms for road transport (EPSRC);
Developing relevant tools for demand responsive transport (EPSRC);
Estimation of a risk profile for operatives and the public from motorway hard-shoulder incursion (EPSRC/Balfour Beatty via CICE);
Towards more autonomy for unmanned vehicles: situational awareness and decision making under uncertainty (EPSRC/BAE Systems);
Assessing the Relationship Between Speed and Casualties (DfT);
Road safety analysis and reporting (Highways England);
School Transport and  GIS: Modelling Policy Options in the Real World (JMP);
Public Transport Accessibility - a GIS Approach (EPSRC – CASE award);
Road transport for the urban poor (British Council)
Logistics and Management of Household Waste Collection (EPSRC/SERCO via CICE).

Research project

Risk assessment methods of autonomous vehicles


Risk assessment methods of autonomous vehicles (AVs) have recently begun to treat the motion of the vehicles as dependent on the context of the traffic scene that the vehicle resides in. In most of the cases, Dynamic Bayesian Network (DBN) models are employed for interaction aware motion models (i.e. models that take inter-vehicle dependencies into account). However, communications between vehicles are assumed and the developed models require a lot of parameters to be tuned. Even with these requirements, current approaches cannot cope with traffic scenarios of high complexity. To overcome these limitations, our research proposes a new methodology that integrates real-time collision prediction as studied by traffic engineers with an interaction-aware motion model for autonomous vehicles real-time risk assessment. Results from a random forest classifier for real-time collision prediction are used as an example for the estimation of probabilities required for the DBN model. It is shown that a well-calibrated collision prediction classifier can provide a supplementary hint to already developed interaction-aware motion models and enhance real-time risk assessment for autonomous vehicles.


Statistical & Mathematical Models, Artificial Intelligence (AI), Sensor Fusion


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School of Civil and Building Engineering Loughborough University Loughborough Leicestershire LE11 3TU United Kingdom

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