Data driven synchronization strategies of a bus line in a transit network

The waiting time of passengers at transfer stations is one of the most important criteria to measure the service quality of public bus transportation. Because of the stochastic nature of traffic, scheduled transfers cannot always occur. This research proposes an online control framework for a bus line using holding, skip-stop and speed change tactics. We build an arc-flow optimization model enumerating all possible tactics within a time horizon. The model minimizes total passenger travel times by improving, among others, transfer times and reducing deviations from the bus schedule. Decisions are based on real-time passenger flow data and travel times. This study is implemented in two steps: deterministic and stochastic The methodology was tested on a case-study of the bus system of the city of Laval, Canada. A simulation framework has been developed, integrating data on smart card transactions and bus locations, to verify the performance and results of the deterministic optimization model. Data generation in the simulation framework is improved using a training set. Different levels of uncertainty are tested on a training set and the resulting optimal parameters are applied to a validation set.