Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

A paratransit-inspired evolutionary process for public transit network design

Public transport companies should run sustainable transit lines and demand oriented services. This paper presents an evolutionary model for the design of demand responsive routes and transport networks. The approach adopts the survival of the fittest principle from competitive developing world paratransit systems with respect to vehicles, market actor characteristics, route patterns and route functions. The model is integrated into a microscopic multi-agent simulation framework, and successfully applied to a naive and a complex scenario. The scenarios include the interaction of paratransit services with conventional public transport. With limited resources paratransit services compete and cooperate with each other to find sustainable routes, which compete or complement existing public transport lines. Besides providing a starting point for paratransit modeling of a region, the approach can also be used to identify areas with insufficient supply of public transport

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

New Signal Priority Strategies to Improve Public Transit Operations

Rapid urbanization is causing severe congestion on road transport networks around the world. Improving service and attracting more travellers could be part of the solution. In urban areas, improving public transportation efficiency and reliability can reduce traffic congestion and improve transportation system performance. By facilitating public buses' movement through traffic signal-controlled intersections, a Transit Signal Priority (TSP) strategy can contribute to the reduction of queuing time at intersections. In the last decade, studies have focused on TSP systems to help public transportation organizations attract more travellers. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for the number of passengers on board as well as the real-time adherence of buses to their present schedules. Two methods have been proposed. First, buses are prioritized based on their load and their adherence to their schedules, while in the second method, the person delay at an intersection is optimized. The optimization approach in the first method uses a specific priority for public transit, while additional parameters are considered in the second method, like residual queue and arrival rate at the intersection. One of this research's main contributions is providing insight into the benefits of these new TSP methods along a corridor and on an isolated signalized intersection. The proposed methods need real-time information on transit operations, traffic signals status and vehicular flows. The lack of readily available infrastructure to provide all these data is compensated by using a traffic simulator, VISSIM, for an isolated intersection and an arterial corridor. The study area simulation results indicated that the new TSP methods performed better than the conventional TSP. For the investigated study area, it was shown that the second method performed better in an isolated signalized intersection, while the first method reduced traffic and environmental indices when used for an arterial corridor. Future research can investigate the effects of the proposed methodology on the urban network by using macrosimulation to see the effects of the proposed TSP on the network. Also, considering conflicting TSP requests in these methodologies could be another area for further research

Passenger Agent and Paratransit Operator Reaction to Changes of Service Frequency of a Fixed Train Line

Public transport companies should run sustainable transit lines and demand oriented services. This paper presents an enhanced evolutionary model, presented earlier, for the design of demand responsive routes and transport networks, The approach adopts the survival of the fittest principle from competitive developing world paratransit systems with respect to vehicles, market actor characteristics, route patterns and route functions. The model is integrated into a microscopic multi-agent simulation framework, and successfully applied to illustrative scenarios. The scenarios include the interaction of paratransit services with conventional public transport. With limited resources paratransit services compete and cooperate with each other to find sustainable routes, which compete or complement existing public transport lines, Besides providing a starting point for paratransit modeling of a region, the approach can also be used to identify areas with insufficient supply of public transport

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

Modelling of interactions between rail service and travel demand: a passenger-oriented analysis

The proposed research is situated in the field of design, management and optimisation in railway network operations. Rail transport has in its favour several specific features which make it a key factor in public transport management, above all in high-density contexts. Indeed, such a system is environmentally friendly (reduced pollutant emissions), high-performing (high travel speeds and low values of headways), competitive (low unitary costs per seat-km or carried passenger-km) and presents a high degree of adaptability to intermodality. However, it manifests high vulnerability in the case of breakdowns. This occurs because a faulty convoy cannot be easily overtaken and, sometimes, cannot be easily removed from the line, especially in the case of isolated systems (i.e. systems which are not integrated into an effective network) or when a breakdown occurs on open tracks. Thus, re-establishing ordinary operational conditions may require excessive amounts of time and, as a consequence, an inevitable increase in inconvenience (user generalised cost) for passengers, who might decide to abandon the system or, if already on board, to exclude the railway system from their choice set for the future. It follows that developing appropriate techniques and decision support tools for optimising rail system management, both in ordinary and disruption conditions, would consent a clear influence of the modal split in favour of public transport and, therefore, encourage an important reduction in the externalities caused by the use of private transport, such as air and noise pollution, traffic congestion and accidents, bringing clear benefits to the quality of life for both transport users and non-users (i.e. individuals who are not system users). Managing to model such a complex context, based on numerous interactions among the various components (i.e. infrastructure, signalling system, rolling stock and timetables) is no mean feat. Moreover, in many cases, a fundamental element, which is the inclusion of the modelling of travel demand features in the simulation of railway operations, is neglected. Railway transport, just as any other transport system, is not finalised to itself, but its task is to move people or goods around, and, therefore, a realistic and accurate cost-benefit analysis cannot ignore involved flows features. In particular, considering travel demand into the analysis framework presents a two-sided effect. Primarily, it leads to introduce elements such as convoy capacity constraints and the assessment of dwell times as flow-dependent factors which make the simulation as close as possible to the reality. Specifically, the former allows to take into account the eventuality that not all passengers can board the first arriving train, but only a part of them, due to overcrowded conditions, with a consequent increase in waiting times. Due consideration of this factor is fundamental because, if it were to be repeated, it would make a further contribution to passengers’ discontent. While, as regards the estimate of dwell times on the basis of flows, it becomes fundamental in the planning phase. In fact, estimating dwell times as fixed values, ideally equal for all runs and all stations, can induce differences between actual and planned operations, with a subsequent deterioration in system performance. Thus, neglecting these aspects, above all in crowded contexts, would render the simulation distorted, both in terms of costs and benefits. The second aspect, on the other hand, concerns the correct assessment of effects of the strategies put in place, both in planning phases (strategic decisions such as the realisation of a new infrastructure, the improvement of the current signalling system or the purchasing of new rolling stock) and in operational phases (operational decisions such as the definition of intervention strategies for addressing disruption conditions). In fact, in the management of failures, to date, there are operational procedures which are based on hypothetical times for re-establishing ordinary conditions, estimated by the train driver or by the staff of the operation centre, who, generally, tend to minimise the impact exclusively from the company’s point of view (minimisation of operational costs), rather than from the standpoint of passengers. Additionally, in the definition of intervention strategies, passenger flow and its variation in time (different temporal intervals) and space (different points in the railway network) are rarely considered. It appears obvious, therefore, how the proposed re-examination of the dispatching and rescheduling tasks in a passenger-orientated perspective, should be accompanied by the development of estimation and forecasting techniques for travel demand, aimed at correctly taking into account the peculiarities of the railway system; as well as by the generation of ad-hoc tools designed to simulate the behaviour of passengers in the various phases of the trip (turnstile access, transfer from the turnstiles to the platform, waiting on platform, boarding and alighting process, etc.). The latest workstream in this present study concerns the analysis of the energy problems associated to rail transport. This is closely linked to what has so far been described. Indeed, in order to implement proper energy saving policies, it is, above all, necessary to obtain a reliable estimate of the involved operational times (recovery times, inversion times, buffer times, etc.). Moreover, as the adoption of eco-driving strategies generates an increase in passenger travel times, with everything that this involves, it is important to investigate the trade-off between energy efficiency and increase in user generalised costs. Within this framework, the present study aims at providing a DSS (Decision Support System) for all phases of planning and management of rail transport systems, from that of timetabling to dispatching and rescheduling, also considering space-time travel demand variability as well as the definition of suitable energy-saving policies, by adopting a passenger-orientated perspective