International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 03 | Mar 2026
p-ISSN: 2395-0072
www.irjet.net
AI-Driven Train Induction Optimization for Kochi Metro Rail Limited Dr. Sandeep Kulkarni1 , Parthivi Singh2 , Shrushti Bhor3 , Shruti Khandve4 Dr. Sandeep Kulkarni Assistant Professor, Department of Computer Science Pune, Maharashtra B.Tech, Student2, Department of Computer Science B.Tech, Student3, Department of Computer Science B.Tech, Student4, Department of Computer Science Ajeenkya DY Patil University Lohegaon, Airport Rd, Charholi Budruk, Pune, Maharashtra ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACT This analysis is on a system involving artificial intelligence to make train schedules improved at Kochi Metro Rail Limited. The system assists in correcting issues such as ensuring that trains run the appropriate volume of miles making people safe delivering contracts with companies advertising on the trains and making use of resources in a wise manner of all 25 trains. We had 8 weeks of testing this system. Used real data to see how it worked. The artificial intelligence system helped improve the schedule of the trains compared to the previous method. doing it by hand. Making train schedules is actually the best thing the artificial intelligence system is capable of. Kochi Metro Rail Limited. The optimization algorithm worked and resulted in a balance in the mileage being. extremely homogenous with 95.2 uniformity. It also cut down the regulatory breaches by a significant margin 87 to be exact. It enhanced the effectiveness of the company in adhering to the guidelines of its branding agreements by 78%. The system has the capability of managing things together such as when the crew is not available as the maintenance is to be done available where a safety check up is necessary [5][7] and what the company must do on behalf of its commercial obligations. When we examined the figures, we learnt that the AI optimizer was faster going with decisions. between 45 minutes and down to 3.2 seconds’ average. The entire optimization algorithm also had made the optimization. The operation became smooth after an increase in the overall operation efficiency by 42%. The optimization algorithm made a difference indeed. The system takes a method to rank things in accordance with there are a lot of elements, such as the fairness of the mileage, its safety, and whether it is compliant with the rules to make the best plan for when to add trains. They tested it in the field at the times of quiet and it worked well. This research assists us to know more of applying intelligence in transportation of cities [8][11] and provides an actual example which can be applied by the representatives of metro rails when they encounter such similar issues. The system can be beneficial to the operators of metro rails since the system assists them in optimization issues. This is because the optimization problems encountered by the operators of the metro railways are difficult to solve. Information on smartness in urban transport systems comes in handy, with them. KEYWORDS: artificial intelligence, optimization of train schedules, the work of a Metro, mileage, technological challenge, safety compliance, Kochi Metro, automated decision-making, multi-constraint, maximization, transport systems, efficiency. 1. INTRODUCTION Metro rail systems in the cities are crucial in the current cities because they carry millions of people daily [8]. This is because one of the functions undertaken is scheduling of train induction which involves the trains that are to be put into service by the depots. This choice has an impact on the quality of services, cost of operation and passenger safety. Manual methods of scheduling prove to be weak in terms of simultaneous consideration of various factors involved in the operation [12][14] including train usage, safety, maintenance times, advertisement agreements, and the availability of resources. Due to these, manual scheduling can be both time-consuming (30-45 minutes) and not necessarily result in the best decisions [14]. Kochi Metro Rail Limited (KMRL) suffers the same hassles in running the metro. The system has 25 trains that run through 13 stations and serves over 75000 passengers in a day. The trains have disparate operational terms which refer to the
© 2026, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 2018