Traffic congestion problems of urban road networks are having a strong impact on economy, due to losses from accidents and delays, and to public health. The recent progress in connected vehicles is expanding the approaches that can be exploited to tackle traffic congestion, particularly in urban regions. Connected vehicles pave the way to centralised real-time re-routing, where a urban traffic controller can suggest alternative routes to be followed in order to reduce delays and mitigate congestion issues in the network. In this work, we introduce a centralised architecture and we compare in simulation a number of approaches that can be exploited for re-routing vehicles.
|Title of host publication||Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence|
|Subtitle of host publication||(AI2020)|
|Publisher||Springer Nature Switzerland AG|
|Number of pages||12|
|Publication status||Accepted/In press - 14 Sep 2020|
|Event||33rd Australasian Joint Conference on Artificial Intelligence - Virtual conference due to COVID-19|
Duration: 29 Nov 2020 → 30 Nov 2020
|Name||Springer Nature Switzerland AG|
|Publisher||Lecture Notes in Computer Science|
|Conference||33rd Australasian Joint Conference on Artificial Intelligence|
|Period||29/11/20 → 30/11/20|
Vallati, M., & Chrpa, L. (Accepted/In press). Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study. In Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020) (Springer Nature Switzerland AG). Springer Nature Switzerland AG.