Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm
place - urban, planning - network design, operations - frequency, economics - benefits, economics - value of time, economics - operating costs
Transit network design, Frequency setting problem, Genetic algorithm, Public transportation, Multi-objective optimization
The multi-objective transit network design and frequency setting problem (TNDFSP) involves finding a set of routes and their associated frequencies to operate in an urban area public transport system. The TNDFSP is a difficult combinatorial optimization problem, with a large search space and multiple constraints, leading to numerous infeasible solutions. We propose an Alternating Objective Genetic Algorithm (AOGA) to efficiently solve it, in which the objective to be searched is cyclically alternated along the generations. The two objectives are to minimize both passengers’ and operators’ costs. Transit users’ costs are related to the total number of transfers, waiting and in-vehicle travel times, while operator’s costs are related to the total required fleet to operate the set of routes. Our proposed GA also employs local search procedures to properly deal with infeasibility of newly generated individuals, as well as of those mutated. Extensive computational experiments results are reported using both Mandl’s original benchmark set and instances with different demands and travel times as well, in order to determine Pareto Frontiers of optimal solutions, given that users’ and operators’ costs are conflicting objectives. The results evidence that the AOGA is very efficient, leading to improved solutions when compared to previously published results.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Arbex, R.O., & da Cunha, C.B. (2015). Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transportation Research Part B: Methodological, Available online 11 August 2015. In Press, Corrected Proof.