Robust Optimization Model for Bus Priority under Arterial Progression
MetadataShow full item record
The purpose of this study is to design a real-time robust arterial signal control system that gives priority to buses while simultaneously maximizing progression bandwidths and optimizing signal timing plans at each intersection along the arterial. The system architecture is divided into three levels. At the progression control level bandwidths are maximized. Existing progression strategies do not use real-time traffic data or use simple mathematical models to estimate traffic evolution. The proposed model eliminates this drawback by using real-time data to develop a neural network model for predicting traffic flows. Rather than using pre-specified values, queue clearance and minimum green times are computed as functions of the predicted queues. To eliminate uncertainty in the prediction due to the long time horizon, robust discrete optimization technique is used to determine the progression bands. At the intersection control level, signal timing plans are optimized subject to bandwidth constraints to allow for uninterrupted arterial flow, and minimum green constraints for driver safety and to discharge average waiting queues. At the bus priority control level, whenever a bus is detected and is a candidate for priority it is granted priority based on a performance index that is a function of bus schedule delay, automobile and bus passenger delays, and vehicle delays, subject to bandwidth and minimum green constraints. Minimum green constraints ensure that other traffic users are not unduly penalized. Bandwidth constraints allow for uninterrupted arterial flow despite a preferential treatment of buses. The performance of the proposed system is evaluated through a case study conducted in a laboratory environment using CORSIM. Results show that the models developed at the three levels are superior to the signal control implemented in the field, and the alternatives that use the off-line MULTIBAND model for progression for all traffic scenarios. Robust optimization was highly effective in reducing control delays, stop times, queues, and bus delays, and increasing throughput and speeds, when traffic volumes were high. The model that integrated bus priority with robust arterial signal control produced the most reductions in bus delays while not causing significant delays to automobiles.