Development and laboratory testing of an intelligent concept for providing priority to buses at signalized intersections without disrupting progression are discussed. The concept used bus position information to predict when in the cycle a bus would arrive at the bus stop and stop line of a signalized intersection and to determine whether a bus needs priority. The strategy used to provide priority was selected on the basis of the estimated arrival time of the bus at the stop line. Priority was provided by using phase extension, phase insertion, and early return strategies without causing the controller to drop from coordination. Implementation of the strategies was accomplished through normal traffic-signal controller commands (such as Ring Force Offs and Phase Holds). Hardware-in-the-loop simulation studies were performed to evaluate the effectiveness of the concept with real traffic-signal controllers. The performance of the intelligent bus priority approach was examined at three volume-to-capacity levels: 0.5, 0.8, and 0.95. Significant reductions in bus travel times were achieved at all three volume-to-capacity levels by using the intelligent bus priority approach. Use of the intelligent bus priority approach resulted in only minor increases in total system stop delay and individual approach stop delays at volume-to-capacity levels of 0.5 and 0.8. The results of the simulation studies performed as part of this study suggested to the researchers that the intelligent bus priority approach could be used at moderate traffic levels (up to volume-to-capacity levels of 0.9 or less) without significantly affecting cross-street delays.
Transit signal priority (TSP) strategy gives transit vehicles preferential treatments to move through an intersection with minimum delay. To produce a good TSP timing, advance planning with enough look-ahead time is the key. This, however, means added uncertainty about bus arrival time at stop bar. In this paper, we proposed a stochastic mixed-integer nonlinear program (SMINP) model as the core component of a real-time TSP control system. The model adopts a novel approach to capture the impacts of the priority operation to other traffic by using the deviations of the phase split times from the optimal background split times. In addition, the model explicitly accounts for the randomness of the bus' arrival time by considering the bus stop dwell time and the delay caused by standing vehicle queues. The SMINP is implemented in a simulation evaluation platform developed using a combination of a microscopic traffic simulator and a commercial optimization solver. Comparison analyses were performed to compare the proposed control model with the state-of-the-practice TSP system [i.e., ring-barrier controller (RBC)-TSP]. The results showed the SMINP has yielded as much as 30% improvement of bus delay compared with RBC-TSP in a single-bus case. In a multiple-bus case, SMINP handles the bus priority request much more effectively under congested traffic conditions. Index Terms-Degree of saturation, mixed-integer nonlinear model, near-side bus stop, rolling horizon, stochastic optimization, transit signal priority (TSP).
One of the issues involved in using microscopic simulation models is the variation in the simulation results. This study examined some of the more popular microscopic traffic simulation models, CORSIM, SimTraffic, and VISSIM, and investigated the variations in the performance measures generated by these models. The study focused on the capacity and delay estimates at a signalized intersection. The effects of link length, speed, and vehicle headway generation distribution were also investigated. With regard to variations in performance measures, the study found that CORSIM yields the lowest variations, whereas SimTraffic yields the highest. The highest variation in each simulation model normally occurs when the traffic demand approaches capacity. It was also found that delays are affected by the link length and speed in simulation models. Such an impact on delays is closely related to the range of speed variations. In general, shorter links and higher link speeds result in lower delays. There is no strong evidence that the headway distribution used to generate vehicles in the simulated network has any effect on capacity and delay estimates. Multiple simulation runs are necessary to achieve an accurate estimate on the true system performance measures. With a 10% error range in estimated delay, two to five runs may be enough for under-capacity conditions, but more than 40 multiple runs may be necessary to accurately estimate delay at, near, or over capacity.
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