Abstract
The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.
Highlights
The field of public transport optimization has attracted immense research interest for more than 50 years
The problem is called Transportation Network Design and Frequency Setting Problem (TRNDFSP) and this paper presents a novel twist on established approaches to create efficient networks
By running the alternating-objective genetic algorithm withwith elitism (AOGAE) algorithm on this network the authors benchmarked their results as presented in Table 1 The algorithm was tested on an
Summary
The field of public transport optimization has attracted immense research interest for more than 50 years. Across the world, city center road networks are buckling from congestion issues that lead to environmental and noise pollution, increased road accident rates and ever decreasing quality of life indexes [5,6,7]. This has turned the sights of researchers, competent authorities, and key stakeholders towards improved public transport systems, as they provide a cost-effective avenue of regulating intercity mobility, stimulating sustainable growth, and increasing citizen satisfaction [8,9]. The problem is called Transportation Network Design and Frequency Setting Problem (TRNDFSP) and this paper presents a novel twist on established approaches to create efficient networks
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