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

Transfer speed and protection are critical factors that influence passengers' willingness to use public transportation. Due to unpredictable traffic patterns, fixed transfer schedules may not always align. This work proposes two online stochastic optimization algorithms for the transfer synchronization problem using the hold, skip-stop, and speedup tactics. First, we design an offline arc-flow model using time-expanded graphs to enumerate all possible tactics. The model minimizes total passenger travel time by reducing transfer times. Then we implement two online stochastic optimization algorithms based on the offline model in a discrete-event 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. The performance of the algorithms is compared using a real dataset from the public transit system of Laval, Canada. The results show significant improvements in both the number of successful transfers and total passenger travel time across 29 bus lines. This supports the practicality of using online stochastic optimization algorithms to solve the transfer synchronization problem in real-world transportation systems.