Real Time Optimisation of Traffic Signals to Prioritise Public Transport

This paper examines the optimisation of traffic signals to prioritise public transportation (busses) in real time. A novel representation for the traffic signal prioritisation problem is introduced. The novel representation is used within an evolutionary algorithm that supports safe solutions which comply with real-world traffic signal constraints. The proposed system finds near-optimal solutions in around 20 s, enabling real-time optimisation. The authors examine a specific junction in Hamburg, Germany, based on real-world traffic data a variety of different problem scenarios ranging from low to exceptional traffic saturations are generated. In collaboration with domain experts, a fitness function is defined to reduce the journey time of a bus while maintaining an overall stable traffic system. Candidate solutions are evaluated using the microscopic traffic simulator SUMO allowing for precise optimisation and addressing of the flow prediction problem. The results show good scaling of the proposed system, with more significant improvements in more congested scenarios. Given the results, future research on bigger and multiple road junctions is motivated.

This work contributes to the field in four ways. Firstly, by defining a real-world problem containing the actual intersection layout and traffic signal parameters. Secondly, by presenting a software design that integrates highly efficient SUMO simulations into an evolutionary algorithm. Thirdly, by introducing a novel representation that allows unconventional solutions while ensuring compliance with traffic signal regulations at all times. Lastly, by testing the suggested approach on various problem scenarios of the real-world problem.