ENABLING RESILIENT TRANSPORT NETWORKS IN RESPONSE TO ROAD TRAFFIC EVENTS

Project aims and objectives

One of the major components of the “Smart City” is an Intelligent Transport System that monitors and reacts to events on the road network and supports both network managers (private and public transport) and network users in making informed choices about alternatives. For example, how can a section of the road network be annotated as liable to flooding and then short-term weather forecasts be used to predict a possible break in the network at that location? Given the break in the road network, can users be rerouted by Variable Message Signs (VMS) or encouraged to change mode e.g. to rail or bus?

The aim of the project is to develop a conceptual model that captures the semantics of the domain. What are the differences, for example, between the characteristics of road works and a road accident that affect how we plan for and respond to each event?

The objectives will include:

1. To perform a comprehensive review of previous research in conceptual models of transport networks.

2. To develop a model using a “breadth-first” approach to consider all aspects of the domain.

3. To prescribe the, potentially, very wide scope of the project by focussing on a particular scenario, such as “a car driver who needs to arrive at a meeting by 10am, whose preferred route is blocked by a road accident caused by icy conditions”; a “depth-first” approach that examines a narrow vertical strip of the domain.

4. To develop a prototype that will utilise disparate data sources and reason with the model to support decision-making.

5. To evaluate the prototype using real-time traffic data. TfGM would be able to provide a test-bed to evaluate the prototype and to help assess the ‘real-world’ application of the conceptual model.

6. To refine the model and the prototype by considering additional scenarios.

The project will be part-funded by Transport for Greater Manchester (TfGM), which has extensive experience in monitoring and modelling traffic in Greater Manchester. Data sources include real-time journey time information, measured using Passive Sensors; historical journey times from GPS and Passive Sensor data; SCOOT data; manual and automatic traffic volume counts and elements of social media (e.g. TfGM twitter feed).

Specific requirements of the project:

Applicants must have a strong background in computer science or related discipline and, in particular, must have an interest in knowledge engineering and data modelling. The candidate should possess excellent programming skills.

The project is in collaboration with Transport for Greater Manchester (TfGM) and the successful applicant candidate will have the opportunity to work alongside TfGM specialists in their city-centre offices. Therefore, he or she must not only be capable of independent working but also be an excellent team player who can communicate well with others.

Student Eligibility: Fully funded PhD studentships at MMU are only available to home and EU students.

Informal enquiries can be made to

Dr Nicholas Gould
Tel: +44(0)161 247 6235
email: n.gould@mmu.ac.uk