Multiobjective Optimization of Greenhouse Gas Emissions Enhancing the Quality of Service for Urban Public Transport Timetabling

This paper presents a multiobjective cellular genetic algorithm to determine bus timetables using multiple vehicle types, considering restrictions of government agencies for public transport systems in the context of smart cities. The first objective is to reduce the greenhouse gas emissions by the minimization of number of vehicles wasting fuel transiting with low ridership. The second one is to minimize number of passengers that cannot move in a certain time-period increasing vehicles overload and waiting time. A set of non-dominated solutions represents different assignments of vehicles covering a given set of trips in a defined route. Our experimental analysis shows a competitive performance of the proposed algorithm in terms of convergence and diversity. It outperforms non-dominated sets provided by NSGA-n.