Dynamic tolling of High-Occupancy/Toll lanes (HOTL) is a challenging task, particularly given the range of policy constraints that could potentially be applicable. Methodological literature on the topic is relatively sparse or is considered proprietary information, particularly with respect to current implementations. A framework was developed for the Ministry of Transportation of Ontario (MTO) that utilizes micro-simulation of short-term future conditions to evaluate alternative tolling rate strategies and select the rate to be applied for the next time interval. In this case, the objective of the selection algorithm was the choice of a toll rate that maximized utilization of the HOTL subject to maintenance of a specified minimum average speed. Short-term future conditions can be anticipated by supplying the micro-simulation model with traffic flow data collected “upstream” of the HOTL. The framework, as conceived, has a high degree of flexibility. It could be applied on-line or simulation trials conducted off-line could be used to generate a look-up table relating toll rates to traffic conditions. Measurements of actual HOTL performance under the chosen toll rate could be recorded and generalized using a neural network. Reinforcement learning or other machine learning methods could be applied either on or off-line to improve the decision-making process. The process, including the dynamic pricing algorithm but excluding generalization or learning capabilities, was implemented in AIMSUN for evaluation and trial application. The current paper describes the operation of the framework and its application to the evaluation of a hypothetical HOVL to HOTL conversion.
