Consistently rising environmental concerns and depleting petroleum resources have accentuated the need of sustainable, energy efficient and clean means of transport. This has provided the impetus to the research and development of clean alternatives for existing public transportation systems. Development of linear motor-propelled, contact-less maglev systems is considered a promising alternative to conventional on-wheel rail transport technology. Maglev technology primarily focuses on improving the performance, speed, fuel economy, driving range and operating cost of the transit system. These parameters vary with the design and efficiency of the electrical system used in maglev-based transportation systems. To present this study, firstly, a detailed survey of the important constituents of a maglev electrical system has been carried out with techno-economic perspectives. Contemporary maglev technologies have then explored along with their respective advantages and limitations. Electrical systems form the heart of maglev systems and, therefore, this paper presents the components of a standard electrical system together with the comparative analysis in terms of present trends, on-going technological advancements and future challenges.

Railway transport has shown a steady growth in passenger numbers over the past 20 years across the UK. Passengers travel with luggage. It has been forecasted that there will be a reduction of “luggage racks-to-seats” ratio in the future passenger train fleet. There are currently no baggage transfer systems at any of the train stations as part of the urban rail transit system in the UK. Hence, the purpose of this study is to investigate the possibility of having a baggage transfer service at Newcastle Central Station, which can serve different travel destinations. A simulation modelling study of different parts of the baggage check-in shop within the railway station is offered. Check-in point, movement of luggage around Newcastle Central, baggage reclaims and storage areas have been contemplated and evaluated using SIMUL8 event-based simulation package. The results for the simulation model developed show that a baggage transfer service at Newcastle Central Station is possible with a mixture of walk-in and online check-in options.

In the design, development, and operation of a rail transport system, all the actors involved use one or more safety methods to identify hazardous situations, the causes of hazards, potential accidents, and the severity of the consequences that would result. The main objective is to justify and ensure that the design architecture of the transportation system is safe and presents no particular risk to users or the environment. As part of this process of certification, domain experts are responsible for reviewing the safety of the system, and are being brought in to imagine new scenarios of potential accidents to ensure the exhaustiveness of such safety studies. One of the difficulties in this process is to determine abnormal scenarios that could lead to a particular potential accident. This is the fundamental point that motivated the present work, whose objective is to develop tools to assist certification experts in their crucial task of analyzing and evaluating railway safety. However, the type of reasoning (inductive, deductive, by analogy, etc.) used by certification experts as well as the very nature of the knowledge manipulated in this certification process (symbolic, subjective, evolutionary, empirical, etc.) justify that conventional computer solutions cannot be adopted; the use of artificial intelligence (AI) methods and techniques helps to understand the problem of safety analysis and certification of high-risk systems such as guided rail transport systems. To help experts in this complex process of evaluating safety studies, we decided to use AI techniques and in particular machine learning to systematize, streamline, and strengthen conventional approaches used for safety analysis and certification.

In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to pre-process the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.

The State Road Transport Undertakings (SRTUs) are the economic providers of mass transport in India. The institutional constraints imposed on the SRTUs result in low productivity and inefficiency. In this fiercely competitive environment, the state-owned public transport industry cannot operate sustainably, showing mediocre performance. With relatively scarce financial resources, high political expectations, and competition between operators, the efficiency and performance of the industry must be improved by optimizing the available resources. In this study, an integrated analytical hierarchy process–goal programming technique considering both operators’ and users’ perceptions is used for performance optimization. The methodology starts with the selection of various performance indicators, considering both operators’ and users’ perceptions. The decision variables are then categorized into user-oriented and operator-oriented. The analytical hierarchy process (AHP), a multicriteria decision-making tool, is then used to evaluate the decision variables and calculate their weights to be used as penalties in goal programming (GP). Pairwise comparison of decision variables on the AHP rating scale was carried out by experts associated with bus transportation and academia. This was used to assign weights to the variables to denote their priority based on their importance. Then, these weights were assigned to the objective function of the GP problem to find a solution that minimizes the weighted sum of deviations from the goal values. As a case study, performance optimization of the Kerala State Road Transport Corporation was undertaken. Twelve decision variables were identified, by taking into account both user and operator perceptions, viz. controllable costs, noncontrollable costs, taxes, staff per bus ratio (fleet operated), safety, accessibility, regularity, load factor, fleet utilization, percentage of dead kilometers to effective kilometers, journey speed, and percentage of cancelled kilometers to scheduled kilometers. The perceived importance of each of these decisive factors from both the users’ and operators’ perspectives was obtained from the experts and prioritized using the AHP. The results indicated that operator cost and staff per schedule were the most important variables for the operators, while safety of travel had the highest weighting according to the users’ perceptions. The optimal solution indicated that increasing the accessibility, safety, and regularity would attract passengers to public transport, which would in turn improve the load factor and influence operators to maximize fleet utilization and reduce cancellation of schedules. Moreover, the solution also suggested that decreasing the staff per bus would further reduce the operating cost. Furthermore, sensitivity analysis was carried out to identify the impact of variations in the decision variables on the performance of the system. The presented method could be used for performance evaluation and optimization of urban rail, metro, and various other public transport systems.