A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology.

Area of Science:

• Urban planning and transportation engineering.
• Intelligent transportation systems (ITS).
• Traffic flow dynamics and optimization.

Background:

• Urban traffic signal control aims to minimize traveler delay and enhance safety for private cars and public buses.
• Current systems often lack multimodal optimization and real-time adaptability.

Purpose of the Study:

• To propose a platoon-based adaptive signal control (PASC) strategy for optimizing urban traffic signals.
• To leverage connected vehicle (CV) data for real-time, multimodal traffic management.
• To minimize total personal delay for all road users, including bus passengers and private vehicle occupants.

Main Methods:

• Developed a platoon-based adaptive signal control (PASC) strategy using connected vehicle information.
• Formulated a mixed-integer linear programming (MILP) model for real-time signal timing optimization.
• Incorporated platoon arrival and discharge dynamics as constraints within the MILP model.
• Utilized VISSIM microsimulation for performance evaluation.

Main Results:

• The PASC strategy reduced bus passenger delay by approximately 40% and automobile delay by 10% compared to SYNCHRO.
• The model demonstrated robustness, showing insensitivity to fluctuations in bus passenger numbers.
• Effective implementation requires a connected vehicle penetration rate of around 20%.

Conclusions:

• PASC offers an effective approach for multimodal traffic signal optimization in urban environments.
• The strategy enhances traffic efficiency and reduces delays for both public transit and private vehicles.
• Connected vehicle technology is crucial for enabling adaptive and coordinated traffic signal control.