Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

In this paper we present a novel tool, using both OpenMP and MPI protocols, for optimising the efficiency of Urban Transportation Systems within a defined catchment, town or city. We build on a previously presented model which uses a Genetic Algorithm with novel genetic operators to optimise route sets and provide a transport network for a given problem set. This model is then implemented within a Parallel Multi-Objective Genetic Algorithm and demonstrated to be scalable to within the scope of real world, [city-wide], problems. This paper compares and contrasts three methods of parallel distribution of the Genetic Algorithm's computational workload: a job farming algorithm and two variations on an ‘Islands’ approach. Results are presented in the paper from both single and mixed mode strategies. The results presented are from a range of previously published academic problem sets. Additionally a real world inspired problem set is evaluated and a visualisation of the optimised output is given

Zone-based public transport route optimisation in an urban network

The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to a input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research

파레토최적해를 이용한 버스네트워크 설계 다목적 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 고승영.Public transportation is a service that provides access to various opportunities and can reduce the mobility gap through efficient network design. However, services are concentrated in a specific area considering economic efficiency, resulting in spatial imbalances in services and inefficiency to users. In this study, bus network design algorithms were presented, including operators, users, and public aspects. An efficiency of operators and users and the competitiveness of public transportation between modes and areas were considered. Toy network was organized according to the urban network topology and demand pattern, and the analysis was performed by applying the algorithm of this study. The applicability of the algorithm was confirmed through the actual network. An improved network could be derived from both operators and the public compared to previous research focused on operational efficiency. Suggested a method to select and apply Pareto optimal according to the planner's judgment. The bus network design algorithm in this study can be used as a means of decision criteria and it can be applied to cities that require a balanced network supply with limited resources.Chapter 1. Introduction ···········································1 1.1 Background ·····································································1 1.2 Research Scope ·····························································4 Chapter 2. Literature Review ·······························7 2.1 Transit Network Design ···········································7 2.2 Unmet Demand ······························································12 2.3 Equity ················································································17 2.4 Algorithm ·········································································26 2.4.1 Multi-objective Optimization ········································26 2.4.2 Local Search ·····································································31 2.5 Summary and Research Direction ·································34 Chapter 3. Methodology ··········································37 3.1 NSGA-II ···········································································37 3.2 Algorithm ·········································································40 3.2.1 Procedure and Network Encoding ······························40 3.2.2 Cross-over and Mutation ··············································43 3.2.3 Local search ······································································44 Chapter 4. Model Formulation ·····························47 4.1 Summary ··········································································47 4.2 Assumption and Variables ·······································48 4.3 Model Formulation ·······················································52 4.3.1 Objective Function ··························································52 4.3.2 Logit Model ······································································54 4.3.3 Traffic Assignment ·························································56 4.3.4 Transit Assignment ······················································58 Chapter 5. Numerical Example ····························61 5.1 Toy Network ································································61 5.1.1 Network Explanation ····················································61 5.1.2 Result Analysis ································································65 5.1.3 Marginal Effect ································································75 5.1.4 Comparison with Previous Research ·························80 5.2 Large Network ······························································83 5.2.1 Network Explanation ······················································83 5.2.2 Result Analysis ································································86 5.2.3 Marginal Effect ································································91 5.2.4 Comparison with Previous Study ·······························92 5.3 Discussion ·······································································94 Chapter 6. Result and Future Research ·········· 97 Reference ·······································································100 Appendix ·······································································110Docto