Abstract
This paper presents new principles for managing signal-controlled intersections. By using machine learning and object detection as substitutes for point detection and offset between intersections, a controller for signal-controlled intersections has been developed using the optimization program UPPAAL. Using the micro simulation program VISSIM, the controller has been tested in four signal-controlled and coordinated intersections in the street Hobrovej in Aalborg, Denmark. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20% in the simulation study.
| Original language | English |
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| Title of host publication | ITS 2018 Conference Proceedings : Transport network operations |
| Number of pages | 10 |
| Volume | 6 |
| Place of Publication | Copenhagen |
| Publisher | ITS World |
| Publication date | 2018 |
| Article number | EU-TP1618 |
| Publication status | Published - 2018 |
| Event | 25th ITS World Congress - Quality of Life - Bella Center, Copenhagen, Denmark Duration: 17 Sept 2018 → 21 Sept 2018 https://itsworldcongress.com/ |
Conference
| Conference | 25th ITS World Congress - Quality of Life |
|---|---|
| Location | Bella Center |
| Country/Territory | Denmark |
| City | Copenhagen |
| Period | 17/09/2018 → 21/09/2018 |
| Internet address |
Bibliographical note
Paper presented in SP7 - Data and informationKeywords
- Signal-controlled intersection
- Machine learning
- Object detection