Document Type : Original Article
Department of Computer Science, Saigon University, Ho Chi Minh City 700000, Vietnam
Faculty of Social Sciences and Humanities, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Metro is considered one of the fastest and most efficient means of transport within the city in different countries, which reduces traffic and pollution and is more cost-effective and time-efficient. Routing transportation systems is one of the critical parts of determining the route and choosing the optimal route with minimal time and cost for users. Most of the methods that led to optimization in routing in the past, both in the airline and on the ground, are based on smart methods. It should be noted that the discovery of knowledge from the data is also essential to predict the path. Hence, data mining operations will also be considered. This research tries to provide an optimal data mining approach and machine learning principles to predict the route and select the optimal path in metro lines with minimum time, best speed, and minor errors in routing. Identifying the factors influencing the scheduling issue have uncertainty. This study tries to provide an optimal method based on data mining and machine learning principles using Fuzzy Logic and Technique for Order Preference by Similarity to Ideal Solution method to predict the priorities of effective factors in metro scheduling in Tehran and select the optimal route in metro lines with a minimum time, the best speed and the least error in routing. According to the findings, the top priorities have a significant influence on the preferred strategy in the metro plan.