Bus Signal Priority (BSP), which has been deployed in many cities around the world, is a traffic signal enhancement strategy that facilitates efficient movement of buses through signalized intersections. Most BSP systems do not work well in transit networks with nearside bus stop because of the uncertainty in dwell time. Unfortunately, most bus stops on arterial roadways are of this type in the U.S. This dissertation showed that dwell time at nearside bus stops could be modeled using weighted least squares regression. More importantly, the prediction intervals associated with the estimate dwell time were calculated. These prediction intervals were subsequently used in the improved BSP algorithm that attempted to reduce the negative effects of nearside bus stops on BSP operations. The improved BSP algorithm was tested on urban arterial section of Bellaire Boulevard in Houston, Texas. VISSIM, a micro simulation model was used to evaluate the performance of the BSP operations. Prior to evaluating the algorithm, the parameters of the micro simulation model were calibrated using an automated Genetic Algorithm based methodology in order to make the model accurately represent the traffic conditions observed in the field. It was shown that the improved BSP algorithm significantly improved the bus operations in terms of bus delay. In addition, it was found that the delay to other vehicles on the network was not statistically different from other BSP algorithms currently being deployed. It is hypothesized that the new approach would be particularly useful in North America where there are many transit systems that utilize nearside bus stops in their networks.