Hybrid predictive control for real-time optimization of public transport systems’ operations based on evolutionary multi-objective optimization
mode - bus, operations - frequency, planning - service improvement, planning - service quality, planning - service rationalisation, technology - intelligent transport systems
Public transport system; Hybrid predictive control; Multi-objective optimization; Genetic algorithms
A hybrid predictive control formulation based on evolutionary multi-objective optimization to optimize real-time operations of public transport systems is presented. The state space model includes bus position, expected load and arrival time at stops. The system is based on discrete events, and the possible operator control actions are: holding vehicles at stations and skipping some stations. The controller (operator) pursues the minimization of a dynamic objective function to generate better operational decisions under uncertain demand at bus stops. In this work, a multi-objective approach is conducted to include different goals in the optimization process that could be opposite. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side, and impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. From the conducted experiments considering a single bus line corridor, we found that the two objectives are opposite but with a certain degree of overlapping, in the sense that in all cases both objectives significantly improve the level of service with respect to the open-loop scenario by regularizing the headways. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing dynamically finding the pseudo-optimal Pareto front and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds.
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Cortés, C.E., Sáez, D., Milla, F., Núñez, A., & Riquelme, M. (2010). Hybrid predictive control for real-time optimization of public transport systems’ operations based on evolutionary multi-objective optimization. Transportation Research Part C: Emerging Technologies, Vol. 18, (5), pp. 757-769.