Online stochastic optimization for real-time transfer synchronization in public transportation networks

Transfer speed and protection are critical factors that affect passengers' willingness to use public transportation. Fixed transfer schedules may not always align due to unpredictable traffic patterns. This work proposes two online stochastic optimization algorithms for the transfer synchronization problem using the hold, skip-stop, and speedup tactics. We design an arc-flow model using time-expanded graphs to enumerate all possible tactics. The model minimizes total passenger travel time by reducing transfer times. Two online stochastic optimization algorithms are implemented in a discrete-event predictive dynamic environment. Decisions are made based on predictions of the future state of the bus network using sampling to generate scenarios from historical and real-time data. We compare the performance of the algorithms on a real dataset from the public transit system of Laval, Canada. The results demonstrate significant improvements in both the number of successful transfers and total passenger travel time across 29 bus lines.