Imperial College London


Faculty of EngineeringDepartment of Civil and Environmental Engineering

Chair in Intelligent Transport Systems



+44 (0)20 7594 6121m.quddus Website




Ms Maya Mistry +44 (0)20 7594 6100




308Skempton BuildingSouth Kensington Campus





I am a Professor of Intelligent Transport Systems. That’s my area of expertise. Over the years, I have actually developed new and transformative approaches. Examples here are to do with map-matching and how they are used in transport, and also route choice models. I also worked extensively in the area of safety, particularly in collision-risk modelling and a lot of my work is underpinned by advanced simulation and the modelling required for those.

My primary research expertise is in Intelligent Transport Systems (ITS) with a specific focus on :

  • Transport safety & emerging technology: analysis, modelling and evaluation
  • Connected and autonomous transport: impacts, operations and planning
  • Classification, modelling and forecasting: Artificial intelligence, machine learning, deep learning & time-series techniques with respect to motorways operations

Recent breakthroughs in technology, digital infrastructure, dynamic mapping and big data computing will transform the way we will plan, undertake, interact, make decisions and use our built environment and transport infrastructure for the movement of people and goods. My approach to understanding, examining and addressing complex transport problems is challenge-led, data-driven and quantitative in nature. This involves the development and application of intelligent sensor fusion algorithms to integrate data from sensors/systems using artificial intelligence techniques (machine learning, neural networks, fuzzy logic), advanced statistical modeling of crash data with the aim of formulating safety policies and micro-simulation techniques.  

Over the last 20 years, I have conducted cutting-edge research leading to innovativeinfluential and transformative outcomes in the areas of transport modelling and simulation, safety analysis and modelling and connected and autonomous vehicles (CAVs). My research in the area of Intelligent Transport Systems (ITS) has led to the development of a series of new navigation, tracking, simulation and path planning algorithms and methods that utilise data from different sensors and systems (such as GPS, vehicle motion sensors, cameras, dynamic vehicle models and spatial GIS road network data). My seminal papers on map-matching algorithms have been influential and highly cited by researchers world-wide. My map-matching algorithms have been implemented by Highways England and different ITS companies worldwide. 

My current research challenges include: (a)  how could we alter our existing road infrastructures to safely operate and accommodate new mobility services supported by disruptive technologies? (b) How could we estimate the impacts of connected and autonomous vehicles on network-level safety and congestion? (c) Could we assist automobile industry in developing intelligent sensor fusion algorithms that would take data from low-cost sensors but provide better quality outputs?  

Example of a research impact: Artificial Intelligence (AI) for crash mapping

Through EPSRC grants and five successfully completed PhD theses, Prof. Quddus has developed innovative and transferable statistical and artificial intelligence (AI) based map-matching algorithms to accurately assign spatial crash data onto road segments, enhancing their quality. Development work funded by Highways England enabled to customise map-matching algorithms for mapping erroneous traffic crashes (STATS19) on the 4,300 miles of motorways and major (trunk) roads. This resulted in two AI-based crash mapping algorithms achieving, for the first time, an accuracy of over 99%.

Highways England (HE), responsible for the 4,300-mile SRN, is charged with reducing by 40% those killed and seriously injured by the end of 2020. To achieve this, it deployed crash mapping and risk models developed by Professor Quddus, which had the following impacts:

(1) Increased Department for Transport (DfT) and HE’s understanding of road safety

(2) Provided HE with the evidence to implement its new Road Safety Delivery Programme

(3) Identified collision hotspots and targeted interventions, which reduced road casualties on the South-West by 17%, equivalent to £44m saving per year

(4) Enabled a road safety intervention toolkit to be developed and applied across the UK’s SRN to reduce collision risk