Towards dynamic coalition formation for intelligent traffic management

TY - BOOK

T1 - Towards dynamic coalition formation for intelligent traffic management

AU - Raphael, J.

AU - Sklar, E.I.

PY - 2018

Y1 - 2018

N2 - Adaptive traffic management aims to adjust the timing of signals at road intersections to ensure smooth travel of vehicles through urban environments. A popular commercial system for handling traffic in this way is SCOOT (Split, Cycle and Offset Optimisation Technique), which involves reading data from sensors embedded in roadways to capture real-time information about traffic volume and making small changes to traffic signal timing in response. SCOOT operates in regions of connected intersections, but the sets of intersections in a region are fixed and the intersections do not communicate with each other. The research presented here takes a multi-agent approach whereby intersections work together in “coalitions” to improve traffic flow, using a market-based mechanism and forming coalitions dynamically as traffic conditions change over time. Experimental results show that this dynamic coalition approach performs better than SCOOT in several types of traffic conditions. © Springer Nature Switzerland AG 2018.

AB - Adaptive traffic management aims to adjust the timing of signals at road intersections to ensure smooth travel of vehicles through urban environments. A popular commercial system for handling traffic in this way is SCOOT (Split, Cycle and Offset Optimisation Technique), which involves reading data from sensors embedded in roadways to capture real-time information about traffic volume and making small changes to traffic signal timing in response. SCOOT operates in regions of connected intersections, but the sets of intersections in a region are fixed and the intersections do not communicate with each other. The research presented here takes a multi-agent approach whereby intersections work together in “coalitions” to improve traffic flow, using a market-based mechanism and forming coalitions dynamically as traffic conditions change over time. Experimental results show that this dynamic coalition approach performs better than SCOOT in several types of traffic conditions. © Springer Nature Switzerland AG 2018.

U2 - 10.1007/978-3-030-01713-2_28

DO - 10.1007/978-3-030-01713-2_28

M3 - Book

VL - 10767 LNAI

BT - Towards dynamic coalition formation for intelligent traffic management

ER -