Hybrid Predictive Control for Operational Decisions in Public Transport Systems

Abstract

In this chapter, a hybrid predictive control strategy is formulated for the real-time optimization of a public transport system using buses. For this problem, the hybrid predictive controller (HPC) is the bus dispatcher, who dynamically provides the optimal control actions to the system to minimize users’ total travel time considering the system’s different components: in-vehicle ride time and waiting time at stops. The HPC framework includes a dynamic objective function and a predictive model of the bus system, which is written in discrete time, where events are triggered when any bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions: holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and is predicted based on both off-line and online information on passenger behavior.

In addition, a multi-objective approach is conducted to include different goals in the optimization process that could be in opposition to one another. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side and the impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing finding the pseudo-optimal Pareto front dynamically and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds.