The design of public transit networks with heuristic algorithms : case study Cape Town

Includes bibliographical references.The Transit Network Design Problem (TNDP) is well-researched in the field of transportation planning. It deals with the design of optimized public transportation networks and systems, and belongs to the class of non-linear optimization problems. In solving the problem, attempts are made to balance the tradeoffs between utility maximization and cost minimization given some resource constraints, within the context of a transportation network. In this dissertation, the design of a public transit network is undertaken and tested for Cape Town. The focus of the research is on obtaining an optimal network configuration that minimizes cost for both users and operators of the network. In doing so, heuristic solution algorithms are implemented in the design process, since they are known to generate better results for non-linear optimization problems than analytical ones. This algorithm which is named a Bus Route Network Design Algorithm (BRNDA) is based on genetic algorithms. Furthermore, it has three key components namely: 1) Bus Route Network Generation Algorithm (BRNGA) - which generates the potential network solutions; 2) Bus Route Network Analysis Procedure (BRNAP) - which evaluates the generated solutions; 3) Bus Route Network Search Algorithm (BRNSA) - which searches for an optimal or near optimal network option, among the feasible ones. The solution approach is tested first on a small scale network to demonstrate its numerical results, then it is applied to a large scale network, namely the Cape Town road network

Optimal Alignments for Designing Urban Transport Systems: Application to Seville

The achievement of some of the Sustainable Development Goals (SDGs) from the recent 2030 Agenda for Sustainable Development has drawn the attention of many countries towards urban transport networks. Mathematical modeling constitutes an analytical tool for the formal description of a transportation system whereby it facilitates the introduction of variables and the definition of objectives to be optimized. One of the stages of the methodology followed in the design of urban transit systems starts with the determination of corridors to optimize the population covered by the system whilst taking into account the mobility patterns of potential users and the time saved when the public network is used instead of private means of transport. Since the capture of users occurs at stations, it seems reasonable to consider an extensive and homogeneous set of candidate sites evaluated according to the parameters considered (such as pedestrian population captured and destination preferences) and to select subsets of stations so that alignments can take place. The application of optimization procedures that decide the sequence of nodes composing the alignment can produce zigzagging corridors, which are less appropriate for the design of a single line. The main aim of this work is to include a new criterion to avoid the zigzag effect when the alignment is about to be determined. For this purpose, a curvature concept for polygonal lines is introduced, and its performance is analyzed when criteria of maximizing coverage and minimizing curvature are combined in the same design algorithm. The results show the application of the mathematical model presented for a real case in the city of Seville in Spain.Ministerio de Economía y Competitividad MTM2015-67706-

A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong

A bus network design problem for Tin Shui Wai, a suburban residential area in Hong Kong, is investigated, which considers the bus services from the origins inside this suburban area to the destinations in the urban areas. The problem aims to improve the existing bus services by reducing the number of transfers and the total travel time of the users. This has been achieved by the proposed integrated solution method which can solve the route design and frequency setting problems simultaneously. In the proposed solution method, a genetic algorithm, which tackles the route design problem, is hybridized with a neighborhood search heuristic, which tackles the frequency setting problem. A new solution representation scheme and specific genetic operators are developed so that the genetic algorithm can search all possible route structures, rather than selecting routes from the predefined set. To avoid premature convergence, a diversity control mechanism is incorporated in the solution method based on a new definition of hamming distance. To illustrate the robustness and quality of solutions obtained, computational experiments are performed based on 1000 perturbed demand matrices. The t-test results show that the design obtained by the proposed solution method is robust under demand uncertainty, and the design is better than both the current design and the design obtained by solving the route design problem and the frequency setting problem sequentially. Compared with the current bus network design, the proposed method can generate a design which can simultaneously reduce the number of transfers and total travel time at least by 20.9% and 22.7% respectively. Numerical studies are also performed to illustrate the effectiveness of the diversity control mechanism introduced and the effects of weights on the two objective values. © 2010 Elsevier B.V. All rights reserved.postprin

Urban Transit Network Design Problems: A Review of Population-based Metaheuristics

The urban transit network design problem (UTNDP) involves the development of a transit route set and associated schedules for an urban public transit system. The design of efficient public transit systems is widely considered as a viable option for the economic, social, and physical structure of an urban setting. This paper reviews four well-known population-based metaheuristics that have been employed and deemed potentially viable for tackling the UTNDP. The aim is to give a thorough review of the algorithms and identify the gaps for future research directions

Some Computational Insights on the Optimal Bus Transit Route Network Design Problem

The objective of this paper is to present some computational insights based on previous extensive research experiences on the optimal bus transit route network design problem (BTRNDP) with zonal demand aggregation and variable transit demand. A multi-objective, nonlinear mixed integer model is developed. A general meta-heuristics-based solution methodology is proposed. Genetic algorithms (GA), simulated annealing (SA), and a combination of the GA and SA are implemented and compared to solve the BTRNDP. Computational results show that zonal demand aggregation is necessary and combining metaheuristic algorithms to solve the large scale BTRNDP is very promising

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

Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

This work utilizes the transit network, which aims to combine the genetic algorithm for analyzing the theory and traffic scheduling based on the traditional methodology. The dynamic methodology is used to schedule the model of transit system, which aims to optimize the demand in the transit network. This model illustrates the methodology of the genetic based transit network (GATN) algorithm to enhance the primary challenges in the transit network. The proposed methodology provides to be significant, with minimizing the objective model of around 27.2%. The model significantly managed to lower the total routes available in the transit network and all travelers related to the time and the transit trip from the initial stage. The significant system obtained using the optimization methodology has 180 routes, 110 less than the initial network, which has a variation by different transit network. This final transmission has been minimized to 33.6% by the proposed methodology in the transit network length and 4.1% reduction in the transfer average. The transition obtained from the multi-level objective function to unique optimization that considers the weighted function proved to be effective

Transit network design with meta-heuristic algorithms and agent based simulation

In this work, a transit network design problem is presented. The problem is identified as a typical large-scale complex system. Subsequently, it is decomposed into its sub-components. The first two sub-components, which encompass the network design and frequency setting problems, are then tackled by means of an innovative solution framework that combines a genetic algorithm with agent-based travel demand modelling. An analysis of results obtained from applying the proposed method to different testing scenarios shows that it is capable of designing transit networks that address the individual and collective perspectives of different stakeholders. Hence it can be used as a viable decision support tool for policy makers in the transportation network sector.https://www.journals.elsevier.com/ifac-papersonlinehj2019Industrial and Systems Engineerin

Optimizing integrated service for a transit route with heterogeneous demand

The methodology developed in this dissertation attempts to optimize integrated service that minimizes the total cost, including user and supplier costs, of a transit route with heterogeneous demand. While minimizing total cost, a set of practical constraints, such as capacity, operable fleet size and frequency conservation, are considered. The research problem is presented in three scenarios, consisting of various service patterns (e.g., all-stop, short-turn and express) under heterogeneous demand. A logit-based model was used to estimate passenger transfer demand. An exhaustive search method was developed to find the optimal solutions for a simplified transit route with six stops, and a Genetic Algorithm (GA) was developed to find the optimal solution for a real-world, large scale transit route. The optimized variables include the combination of service patterns, the associated service frequencies, and stops skipped by the express service. A six-stop transit route was designed and analyzed via a proof-of-concept demonstration to ensure that the developed models are capable of finding the optimal solutions. A sensitivity analysis was conducted, which enables transit planners to quantify the impact of various model parameters (e.g., user value of time, vehicle capacity, operating cost, etc.) to the decision variables and the objective function. Finally, the developed models and solution algorithm were applied to optimize integrated service for a real world bus route in New Jersey