Abstract
Transit Signal Priority (TSP) has been proposed and studied as a control strategy that offers preferences to transit vehicles at signalized intersections. Conventionally, many challenges have been identified that are preventing the TSP to be widely deployed, for example, adverse effect on side streets, and uncertainty of benefit. Closer investigation on these challenges reveals that these shortcomings are mainly caused by the fact that the logic of conventional TSP is based on data collected from the past instead of the present. If with real time data, many uncertainties can be eliminated, and correspondingly, TSP could perform better with higher reliability. The emerging new system known as connected vehicles is able to feed TSP with present data and also create many other possibilities for the TSP. In a connected vehicles environment, diagnostic sensors are installed on every vehicle to collect data and data are being transmitted wirelessly between vehicles and nearby infrastructures. It no longer has to rely on conventional data collection equipment, like loop detector or video detections, and it collects much more information than the conventional ways. Measurements that are previously unknown are now available, which include but not be limited to: vehicle speeds, positions, arrival rates, rates of acceleration and deceleration, queue lengths, stopped time and so on. A system of bus priority techniques is developed, taking advantages of the resource provided by Connected Vehicles (CV) technology, including two-way communications between the bus and the traffic signals, accurate bus location detection and prediction, and other information. The TSP logics allow cooperative control that traffic signal and transit bus work together. The cooperation requires a bus to travel at a reasonable speed which is recommended based on road geometry, normal signal timing plan and remaining/expected queue. The TSP strategy used is the green time reallocation, which only moves green time instead of adding extra green time. The TSP is also designed to be conditional on certain criteria. Delay per person is used as one of the most important criterion to decide whether TSP shall be granted. The developed TSP techniques are able to accommodate: single TSP request at an isolated intersection, bus merging at a nearside bus stop, multiple conflicting TSP requests at an isolated intersection and bus progression along a corridor. The logic developed in this research is evaluated in two ways: with analytical and microscopic simulation approaches. The proposed TSP techniques are usually compared against two scenarios: no TSP and conventional TSP. The measures of effectiveness (MOE) used are bus delay and per person delay of all travelers. Simulation-based evaluation results show that, compared to conventional TSP, the proposed TSP logic reduces bus delay of a single TSP request between 9% and 84%, minimizes bus delay during conflicting requests between 5% and 48%, decreases delay of buses progressing along a corridor between 35% and 68%, and cut back up to 30% of delay buses lose at merging from nearside bus stops. The range of improvement corresponding to the four different v/c ratios tested, which are 0.5, 0.7, 0.9 and 1.0. In most cases, no significant negative effects are caused by the proposed TSP logic.
Transit Signal Priority (TSP) has been proposed and studied as a control strategy that offers preferences to transit vehicles at signalized intersections. Conventionally, many challenges have been identified that are preventing the TSP to be widely deployed, for example, adverse effect
Transit Signal Priority with Connected Vehicle Technology
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