High-frequency transit systems are essential for the socioeconomic and environmental well-being of large and dense cities. The planning and control of their operations are important determinants of service quality. Transit operators are increasingly adopting data collection devices that enable real-time monitoring of vehicle locations and demand, but existing models and current practice limit the utility of this information. This research develops new concepts, frameworks, and models for real-time optimization of operations, utilizing both historical and real-time information originating from connected data collection devices, including automated vehicle location, automated fare collection, and automatic passenger counting systems.

Previous control strategies either do not forecast system states or rely on forecasts based on running times and demand assumed to be static. This research develops an optimization model for holding-based control that incorporates dynamics, producing a holding policy that accounts not only for the current state of the system, but also for expected changes in running times and demand, due to both exogenous and endogenous dynamics. This information advantage can lead to improved performance when a transit service faces typical changes in running times and demand over time, as well as potentially disruptive events such as signal failures, disabled rolling stock, and demand surges. Anticipatory control policies allow the transit service to react before disruptions develop. It is shown that information about dynamics is particularly valuable when it leads to better predictions of capacity being reached. Although headway and optimization-based control strategies generally out- perform schedule-adherence strategies, high-frequency operations are mostly planned with schedules, in part because operators must observe resource constraints (neglected by most control strategies) while planning and delivering service. This research develops a schedule-free paradigm for high-frequency transit operations, in which trip sequences and departure times are optimized in real-time, employing stop-skipping strategies and utilizing real-time information to maximize service quality while satisfying operator resource constraints. Following a discussion of possible methodological approaches, a simple methodology is applied to operate a simulated transit service without schedules. Results demonstrate the feasibility of the new paradigm and suggest possible methodology improvements.