01456596

Component

http://www.trb.org/Publications/Blurbs/168134.aspx

Artificial intelligence (AI) methods have been frequently employed to solve complicated control and optimization problems in many different disciplines. Traffic signal control and optimization is no exception. A wide range of AI methods have been introduced into this area to develop innovative traffic control strategies and fine tune signal timing plans and parameters. These AI applications are summarized into the following four categories and detailed in this article: evolutionary algorithms, fuzzy logic control, artificial neural networks control, and reinforcement learning and agent-based control.

01456594

English

pp 11-20

2012-11

Transportation Research Circular

Web

References

Artificial intelligence; Fuzzy logic; Neural networks; Optimization; Traffic signal timing

Agent based models; Evolutionary algorithms; Reinforcement learning

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Dec 10 2012 9:10AM

• Advantages and Limitations of Artificial Intelligence
• Agent-Based Modeling and Simulation
• Analysis of Public Transportation Planning and Operations
• Artificial Intelligence and Microscopic Traffic Simulation Models: Applications to Parameter Calibration and Origin–Destination Estimation
• Design and Construction of Transportation Infrastructure
• Difference Between Artificial Intelligence and Traditional Methods
• Neural Networks for Travel Time Prediction on Interrupted Flow Facilities
• Ramp Metering
• Short-Term Traffic and Travel Time Prediction Models
• Transportation Safety Analysis
• Transportation Security
• Travel Behavior Research
• Urban Travel Demand Forecasting