TY - JOUR
T1 - C-MP
T2 - A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework
AU - Ahmed, Tanveer
AU - Liu, Hao
AU - Gayah, Vikash V.
N1 - Publisher Copyright:
© 2025
PY - 2025/10
Y1 - 2025/10
N2 - Coordinated traffic signals seek to provide uninterrupted vehicular flow through a series of closely spaced intersections, typically using pre-defined fixed signal timings and offsets. However, coordination is typically not possible when using adaptive traffic signals that dynamically change signal timings based on observed traffic conditions, particularly in decentralized systems where these decisions are made independently at each intersection. To alleviate this issue, this paper introduces a novel Max Pressure-based traffic signal framework that specifically seeks to provide coordination even under decentralized decision-making. The proposed Coordinated Max Pressure (C-MP) algorithm uses the space mean speeds of vehicles – obtained from detectors or in a connected vehicle (CV) environment – to explicitly detect freely flowing platoons of vehicles and prioritizes their movement along a corridor. Specifically, upstream platoons are detected, and their weight in the MP framework is increased to provide priority, while downstream platoons are detected, and their weight is reduced to ensure smooth traffic flow across corridors. The study analytically proves that C-MP maintains the desirable maximum stability property, while micro-simulation analyses conducted on an arterial network demonstrate its ability to achieve a larger stable region compared to benchmark MP control policies. Simulation results also reveal that the proposed control algorithm can effectively coordinate traffic signals in both directions along an arterial without explicitly assigned offsets or constraints. The results also reveal C-MP's superiority to benchmark coordination strategies in reducing travel time, and fuel consumption both at the corridor level and the network level by balancing the negative impact imparted to vehicles in the minor direction. The proposed algorithm also functions effectively when a subset of vehicles provide input to the controller, as would be the case in a partial CV environment.
AB - Coordinated traffic signals seek to provide uninterrupted vehicular flow through a series of closely spaced intersections, typically using pre-defined fixed signal timings and offsets. However, coordination is typically not possible when using adaptive traffic signals that dynamically change signal timings based on observed traffic conditions, particularly in decentralized systems where these decisions are made independently at each intersection. To alleviate this issue, this paper introduces a novel Max Pressure-based traffic signal framework that specifically seeks to provide coordination even under decentralized decision-making. The proposed Coordinated Max Pressure (C-MP) algorithm uses the space mean speeds of vehicles – obtained from detectors or in a connected vehicle (CV) environment – to explicitly detect freely flowing platoons of vehicles and prioritizes their movement along a corridor. Specifically, upstream platoons are detected, and their weight in the MP framework is increased to provide priority, while downstream platoons are detected, and their weight is reduced to ensure smooth traffic flow across corridors. The study analytically proves that C-MP maintains the desirable maximum stability property, while micro-simulation analyses conducted on an arterial network demonstrate its ability to achieve a larger stable region compared to benchmark MP control policies. Simulation results also reveal that the proposed control algorithm can effectively coordinate traffic signals in both directions along an arterial without explicitly assigned offsets or constraints. The results also reveal C-MP's superiority to benchmark coordination strategies in reducing travel time, and fuel consumption both at the corridor level and the network level by balancing the negative impact imparted to vehicles in the minor direction. The proposed algorithm also functions effectively when a subset of vehicles provide input to the controller, as would be the case in a partial CV environment.
UR - https://www.scopus.com/pages/publications/105014454068
UR - https://www.scopus.com/inward/citedby.url?scp=105014454068&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2025.103308
DO - 10.1016/j.trb.2025.103308
M3 - Article
AN - SCOPUS:105014454068
SN - 0191-2615
VL - 200
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
M1 - 103308
ER -