Transit signal priority (TSP) is a common strategy to improve bus right-of-way at signalized intersections. However, TSP systems have several challenges, such as negative externalities for non-transit users, and handling conflicting priority requests. Considering recent advances in connected vehicle technology, we propose a user-based signal priority strategy (UST) to facilitate bus movement at intersections while minimizing adverse effects to non-transit users. Additionally, we extend UST by minimizing bus scheduled delay (UST-SD) to compensate bus delay that is caused by network congestion. We compare UST and UST-SD with a conventional TSP ring barrier controller (RBC) at an isolated signalized intersection in a microscopic simulation environment. The findings show that the proposed strategy improves user and vehicle performance measures while providing priority for buses.

The development of data-driven smart arterial systems that enables reduction in delays and increases the travel time reliability is a valuable tool for improving of arterial performance. Understanding the synergies and differences between speed data sets and traffic signal controller data is necessary for the efficient deployment of Intelligent Transportation Systems (ITS). It is not always feasible to fully instrument an intersection that provides data on optimal performance metrics. Therefore, it is necessary to establish a baseline performance metric for a given corridor to develop an ITS management plan. To that effect, this study conflated crowd-sourced anonymous probe vehicle data on vehicles trajectories and speeds with high-resolution traffic signal data sets. Interdependencies between the two datasets were examined and baseline corridor performance metrics were established. The analysis included an evaluation of the data sets for a 7.3-mile corridor in Burlington County, New Jersey (AADT ranging from 10,000 45,000). A GPS-equipped test vehicle was used to establish reliability of probe-vehicle data, which was then compared to near-term performance measures derived from high-resolution traffic signal data. Based on the analysis of approximately 1.7-million probe vehicle data points using visualization tools, the study demonstrated that the integration of multiple data sets provides a viable mechanism for the development of reliable, visually intuitive, arterial performance metrics. The study results also indicate that long term speed data from anonymous probe vehicle data could be used to evaluate traffic signal data to measure arterial performance measurement.