01456603

Component

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

Ramp metering is a control strategy that regulates the frequency of vehicles entering the freeway at entrance ramps. Operated by either single or systemwide stop-and-go type traffic signals, the objective of ramp metering is to maintain optimum control of the freeway and prevent operational breakdowns through limiting the rates of entering vehicles at entrance ramps or freeway-to-freeway connector ramps. For both pretimed and traffic-responsive local and systemwide control strategies, the underlying idea of a ramp metering algorithm is to balance demand and capacity of the freeway through well-defined objective functions and constraint sets. In this regard, linear programming has been widely used since the emergence of the ramp metering concept in the 1960s. Since then, transportation problems have become increasingly complex as their scope of analysis expanded rapidly beyond the traditional domain. These problems are characterized by: a large number of factors involved; parametric associations among the factors that are unfathomable; the huge amount of incomplete data that are encompassed; and many objectives and constraints that are so intertwined that the priorities among the stakeholders are blurry. Given that complex problems are difficult to solve using conventional methodologies, there has been a growing interest in employing artificial intelligence (AI) paradigms to address transportation issues to improve operation, safety, and efficiency of transportation systems. This article examines the application of AI to ramp metering.

01456594

English

pp 70-75

2012-11

Transportation Research Circular

Web

References

Artificial intelligence; Ramp metering

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

TRIS, TRB, ATRI

Dec 10 2012 11:13AM

• 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
• Short-Term Traffic and Travel Time Prediction Models
• Traffic Signal Timing and Optimization
• Transportation Safety Analysis
• Transportation Security
• Travel Behavior Research
• Urban Travel Demand Forecasting