A New Algebraic Approach to Decision Making in a Railway Interlocking System Based on Preprocess

Abstract

The safety of railway networks is a very important issue. Roughly speaking, it can be split into safety along lines and safety of railway facilities such as stations, junctions, yards, etc. In modern networks the safety along lines is controlled by automatic block systems that do not give clearance to trains to enter a section (block) until the latter is detected to be unoccupied. Meanwhile, the safety within railway facilities is supervised by railway interlocking systems. Decision making in a railway interlocking is a very important issue which is considered to be very labour-intensive. Decision-making in both automatic block systems and railway interlocking systems, unlike road traffic light systems, is not based on time (they are not scheduling problems) but in space. Basically, two different trains should never be allowed to access the same section (whatever time has passed). There are many different approaches to automate decision-making in railway interlocking systems. The classic approaches are offline: only certain routes are allowed and their compatibility is decided in advance. Meanwhile, modern approaches make decisions in real time and are independent from the topology of the railway network, but can be applied only to small or medium size railway networks. Nevertheless, these last approaches have the following drawbacks: the performances are very dependent on the number of trains in the railway network; and are unsuitable to large networks since they take long time to be run. On the other hand, algebraic approaches based on computer algebra concepts have been used in artificial intelligence for implementing expert systems. In this paper we present a completely new algebraic model, based on these concepts of computer algebra that overcomes these drawbacks: the performance of our approach is independent of the number of trains in the railway network and also is suitable for large railway networks.