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

A Multilayer Network Approach for the Bimodal Bus–Pedestrian Line Planning Problem

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseIn this paper, we formulate and solve the urban line planning problem considering a multilayer representation of a bimodal transportation network. Classical formulations are usually constructed over a planar network, which implies the need to introduce several strong non-linearities in terms of frequencies when modeling transfer times. In the proposed network representation, each candidate line is stored in a specific layer and the passengers’ movements for each origin– destination pair are modelled considering a strategy subgraph, contributing to a sparse model formulation that guarantees feasibility and simplifies the assignment process. The methodology is first tested using the Mandl network, obtaining results that are comparable in terms of quality with the best metaheuristic approaches proposed in the scientific literature. With the aim of testing its applicability to large scenarios, the proposed approach is then used to design the main urban transit network of Seville, a large scenario with 141 nodes and 454 links, considering artificial unfavorable demand data. The reasonable computation time required to exactly solve the problem to optimality confirms the possibility of using the multilayer approach to deal with multimodal network design strategic problems.Universidad de SevillaJunta de AndalucíaFondo Europeo de Desarrollo Regional (FEDER) US-138165

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

Transit Network Design using GIS and Metaheuristics in Sanandaj, IRAN

The public transit system in Sanandaj has been under review and modification for the last several years. The goal is to reduce the traffic congestion and the share of private car usage in the city and increase the very low share of the public transit. The bus routes in Sanandaj are not connected. There is no connected transit network with the ability to transfer between the routes in locations outside of the downtown terminal. The routes mostly connect the downtown core directly to the peripheries without providing travel options for passengers between peripheries. Although there has been some improvement in the transit system, but lack of service in many populated districts of Sanandaj and town nearby makes the transit system unpopular and unreliable. This research is an attempt to provide solutions for the transit network design (TND) problem in Sanandaj using the capabilities of GIS and artificial intelligence methods. GIS offers several tools that enables the decision-makers to investigate the spatial correlations between different features. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modeling functionalities. The visual ability of GIS is used to generate TNDs. Many studies focus on artificial intelligence as the main method to generate the TNDs, but the focus of this research is to combine GIS and artificial intelligence capabilities in order to generate a multi-objective GIS-based procedure to construct different bus network designs and explore and evaluate them to find the suitable transit network alternative. The GIS-based procedure results will be assessed and compared with the results of metaheuristic approaches. Metaheuristic methods are partial search procedures that may provide sufficiently good solutions to an optimization problem characterized by incomplete information or limited computation capacity (Talbi, 2009). Yang, Cui, Xiao, Gandomi, and Karamanoglu (2013) classified metaheuristic methods into two groups: single-agent procedures (e.g., simulated annealing algorithm involves one agent navigating in the environment), and multiple agents (e.g., population-based genetic algorithm, and swarm intelligence methods). This study focuses on swarm intelligence methods, such as ant colony optimization and honeybee algorithm. These methods provide a multi-objective assessment of the TND scenarios generated by GIS applications. The outcome of this study will help us to find the optimal solutions for the TND in Sanandaj

Transit Route Planning to Improve Accessibility: A Reinforcement Learning Approach

Public transport has a key role in social sustainability by reducing social isolation and improving access to essential opportunities. The conventional approaches to transit network and service planning do not consider the social role of public transport, and attempt to maximise the cost savings of users and the operational efficiency of operators. Cost-based network design tends to allocate more transit routes and/or services to high population density areas. This can create transit service gaps between high and low population density areas and leave some people poorly served with low accessibility. This study proposed a new algorithm for transit network design by incorporating a transit accessibility measure in the routing optimisation. Transit accessibility refers to the ease of reaching opportunities from a location by public transport. The proposed approach aims to improve the transit connectivity between residential zones and major activity centres, which contain a range of opportunities. This study also developed a transit need index and integrated it into route planning to account for the socio-demographic characteristics of potential transit users in the network design to benefit those who need the transit service to reach essential opportunities. This index is formulated by combining selected socio-economic and demographic variables and then integrating them with the transit accessibility measure as the optimisation objective. This study developed a reinforcement learning method for the purpose of the bus network and service planning. This study used reinforcement learning to address the complicated interactions of associated features in bus network and service planning. Reinforcement learning methods achieve a complex goal or optimise long-term performance over many experiences. As reinforcement learning can work in changing environments, reinforcement learning methods effectively solve sequential decision-making problems such as bus route and service planning. The reinforcement learning method was examined to validate the effectiveness of adapting the transit accessibility measure in transit route planning, with and without the transit need index, on the existing bus network in a case study area. The existing bus network in the case study area of Penrith local government area in western Sydney, Australia, has 56 bus routes and 1,940 stops, which provides a realistic and challenging test environment. The performance results indicate that the proposed algorithm, which adopts the measure of transit accessibility combined with the transit need index as an optimisation objective, can both improve overall transit accessibility and provide an appropriate level of service across areas