A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

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

The Gamut Looks at Cleveland, Special Edition, 1986

CONTENTS OF SPECIAL ISSUE, 1986 Candace S. Shireman: Marble Mirrors of Society: Cemeteries in Cleveland, 1797-1984, 1 From Issue Number 11, Winter 1984 Arthur Geoffrion: Landmark Complex, 15 From Issue Number 4, Fall 1981 Elizabeth Kirk: Severance Hall, Cleveland’s Temple of Music, 19 From Issue Number 18, Spring/Summer 1986 Walter C. Leedy, Jr.: Cleveland’s Terminal Tower – The Van Sweringens’ Afterthought, 39 From Issue Number 8, Winter 1983 Mary Peale Schofield: Meade and Hamilton’s Livable Cleveland Houses, 63 From Issue Number 6, Spring/Summer 1982 Sara Ruth Watson and John R. Wolfs: Movable Bridges over the Cuyahoga River, 79 From Issue Number 3, Spring/Summer 1981 Michael Samerdyke: Spellbound at the New Mayfield, 88 From Issue Number 18, Spring/Summer 1986https://engagedscholarship.csuohio.edu/gamut_archives/1036/thumbnail.jp