Operating cost and quality of service optimization for multi-vehicle-type timetabling for urban bus systems

In this paper, we propose a timetable optimization method based on a Multiobjective Cellular genetic algorithm to tackle the multiple vehicle-type problems. The objective is to determine bus assignment in each time period to optimize a quality of service and transport operating cost. The quality of service, represented by the unsatisfied user demand, guarantees a good experience in terms of comfort, safety, availability, improving effects on how passengers perceive wait times. The operational cost contributes to reducing the traffic jams, the flux of unfilled vehicles and fuel consumption, helping to diminish the negative environmental impact. With the operation data of Los Angeles bus route 217 northbound, at peak and off-peak hours, we obtain a set of non-dominated solutions that represent different assignments of vehicles covering a given set of trips in a defined route. The experimental analysis based on several quality indicators, like Hypervolume, Spread, ε-Indicator, and Set Coverage, indicates that our algorithm is a competitive technique comparing with well-known techniques presented in the literature.