In this paper, C-MIXCROS and D-MIXCROS, two feedback-based coordinated ramp metering strategies that explicitly consider ramp queues, are proposed. They are evaluated using both macroscopic (Rutgers Macroscopic Simulation Environment) and microscopic (PARAMICS) simulation models (on an 11-mile-long corridor of I-295 in South Jersey) under different demand conditions. In addition to the newly proposed coordinated ramp metering strategies, a well-known coordinated strategy (METALINE [Papageorgiou et al. Transport Res. 1990;24A:361–370]) and three other local strategies (ALINEA [Papageorgiou et al. Transportation research record, No. 1320, Washington, D.C.: TRB, National Research Council; 1991. p. 58–64], New Control [Kachroo and Ozbay. Feedback ramp metering in intelligent transportation systems. New York: Kluwer Academics; 2003], and MIXCROS [Kachroo and Ozbay. Feedback ramp metering in intelligent transportation systems. New York: Kluwer Academics; 2003) are also implemented using the same network and results are compared. The proportional-derivative state feedback control logic and direct regulation of on-ramp queues are employed in the derivation of this new proposed coordinated ramp metering strategy. The simulation results are consistent with the macroscopic simulation results, where D-MIXCROS and C-MIXCROS both perform more competently than all other control strategies tested for every demand scenario. The deteriorating effect of enormous on-ramp queues on the total travel time is observed especially in METALINE results; the total travel time for METALINE is approximately 22% greater compared with C-MIXCROS. MIXCROS also successfully maintains the on-ramp queues at a reasonable level for each ramp. However, because it is a local ramp metering strategy, coordinated versions of MIXCROS are observed to be more beneficial both for the ramp system and at the network level.
Coordinated Feedback-Based Freeway Ramp Metering Control Strategies “C-MIXCROS and D-MIXCROS” that Take Ramp Queues into Account
Complex Netw Dyn Syst
Advances in Dynamic Network Modeling in Complex Transportation Systems ; Chapter : 3 ; 67-88
2013-02-12
22 pages
Article/Chapter (Book)
Electronic Resource
English
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