Postgraduate research project

Crowd sourced traffic control strategies

Funding
Competition funded View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

This project will create a simulated road environment game, to understand how people prioritise different road users, and train the next generation of artificial intelligence based control algorithms to make traffic lights operate the way that people want them to.

The algorithms that control traffic lights are effective at maintaining safety while maximising the number of vehicles that can pass through road junctions, responding to real time variation in approaching traffic. But common control approaches fundamentally assume that all approaching objects are the same. But should we give more (or less) priority to Cyclists? Pedestrians? Buses? Heavy goods vehicles? Driverless cars? Electric cars? Cars with more people in them? Expensive cars? 

It is widely considered that existing algorithms fail to sufficiently account for the increasing heterogeneity of objects using road junctions and therefore do not appear to operate in ways that those road users expect. This project therefore first seeks to understand how the general public want traffic lights to work through developing a traffic control simulation app to crowd sources preferences and operating strategies. This data will then be used to calibrate new control algorithms based on peoples’ preferences and contrast these with existing approaches to understand the impacts on delays of providing more socially acceptable systems.