چكيده به لاتين
Among the transportation systems, the railway is the most economical and safest means of transporting cargo and passengers, which reduces traffic and minimizes pollution for the environment. A number of railway stations in our country more They have freight usage and less travel usage. Today, data collection is of great importance in most service locations such as railways. Research on this data and obtain useful results and patterns in relation to stations are the reasons for using this data. But the large volume of raw data and their high complexity is a problem that prevents acceptable and reliable results. These trains are mostly freight and are used to transport goods, so the railway stations are connected to the ports. In these ports, loading is done and then it is transported by rail all over the country. One of the problems in railway stations is congestion and congestion of loads and trains, which disrupts the process of moving and loading, and reduces the quality of work and we move away from optimal conditions. These problems can also weaken the connection of this railway station with other stations, reduce the flow between stations and increase costs. So, we face four important challenges in this regard: 1 - Communication between stations 2- Congestion at stations 3- Delay at stations 4- Quality of performance at stations. The most important factor in this case is congestion, which has a great impact on communication and performance quality (input to output ratio - time or cost) and finding a relationship between these factors in a large amount of information, it is very difficult to use Data mining and machine learning techniques found and improved them. The use of new techniques and tools can provide a different perspective than what has been described by statistical techniques by railway safety operators. Therefore, with new data mining methods, we can have better choices in scheduling and scheduling trains due to their delays. Due to the large number of trains, wagons, different arrival times at the station, separation from the station, stop time, delay time, secondary delay, etc., the need for an automated system to reduce costs and improve train classification is felt. IoT approaches can also be used to create an intelligent system that, after finding relationships, regularly provides them with a specific schedule for moving, loading, and unloading trains according to their daily train schedule and delays.
