PROJECT TITLE :

Macroscopic Modeling and Control of Reversible Lanes on Freeways

ABSTRACT:

This paper proposes a macroscopic model and 2 management algorithms for the dynamic operation of reversible lanes on freeways. The proposed model is an extension of the second-order traffic flow model METANET. The reversible lanes are modeled like variable lane drops (taking into consideration that the cars in the closed/opened lanes would like a sure time to depart/enter the corresponding segments). Based on this model, two types of dynamic controllers have been developed. The primary one is an simple-to-implement logic-based controller that takes under consideration the congestion lengths generated by the reversible lane bottleneck and uses this information for the dynamic operation of the lanes. The other is a discrete model predictive management that minimizes the whole time spent of the modeled network at intervals some constraints for the utmost values of the generated bottleneck queues. The discrete optimization is meted out via evaluation of the cost operate for all the leaves in a reduced search tree. The proposed model and control algorithms are simulated and tested using loop detector data collected over a section of the SE-30 freeway in Seville, Spain. The modeled network includes the Centenario Bridge, that may be a bottleneck with a reversible lane that creates recurrent congestion during the morning rush-hour amount. The results show that the proposed model is in a position to breed hold up thanks to the reversible lanes and that each one the proposed controllers (that can be computed in an exceedingly short time) substantially reduce this congestion.