Bus transit operations are influenced by stochastic variations in a number of factors (e.g., traffic congestion, ridership, intersection delays, and weather conditions) that can force buses to deviate from their predetermined schedule and headway, resulting in deterioration of service and the lengthening of passenger waiting times for buses. Providing passengers with accurate bus arrival information through Advanced Traveler Information Systems can assist passengers’ decision-making (e.g., postpone departure time from home) and reduce average waiting time. This article develops a set of regression models that estimate arrival times for buses traveling between two points along a route. The data applied for developing the proposed model were collected by Automatic Passenger Counters installed on buses operated by a transit agency in the northeast region of the United States. The results obtained are promising, and indicate that the developed models could be used to estimate bus arrival times under various conditions.