Track Degradation Prediction Models, Using Markov Chain, Artificial Neural and Neuro-Fuzzy Network

Track condition is one of the most important parameters affecting the track maintenance management. In order to obtain a good track maintenance management system, it is necessary to predict track condition through the time. In this study, the track’s state will be defined in terms of the Combine Track Record index (CTR) rating which can vary from 0 to 100 where 100 denotes the best possible track condition and the states are defined as five intervals of CTR, similar to those used in Iranian Railways. Four models are built and testes for the prediction of track quality, one conventional model suggested by ORE, and three new model, using markov chain, artificial neural network and neurofuzzy network. The data for our empirical application was collected from Iranian Railways network. Comparisons of the models show that all three proposed new models predict track deterioration better than the ORE model. Introduction The railway is a branch of the transportation system that is very expensive to construct but it has a long life and low operating costs. Therefore, the asset value is very high, which also leads to the possibility that maintenance might be expensive. Like other infrastructure with investment costs in the construction phase, maintenance plays a crucial role in the long-term cost effectiveness, and so maintenance management is one of the most important parts of the railway systems. Track condition is one of the most important parameters affecting the track maintenance management. In order to obtain a good track maintenance management system, it is necessary to predict track condition through the time. The cost of maintaining a track depends directly on its condition or "state". Maintenance and rehabilitation funds are often allocated to tracks that are in the worst state or that exhibit an accelerating rate of deterioration. Some of factors that affect the rate of deterioration of tracks include: traffic loads, weather, and construction materials. The track conditions vary considerably even when such contributing factors are similar. Therefore, it is important that a procedure that can accommodate such randomness be incorporated into a track management system. Several approaches and methods for predicting railways conditions have been proposed, and based on these, a considerable number of maintenance planning tools have been developed for railways systems in North America and Europe. A nonlinear regression model based on a product of the power functions has been proposed by the Office for Research and Experiments (ORE) of the International Union of Railways [9] to predict track deterioration. Having track degradation models, operations research techniques are commonly used to optimize track maintenance activity. Such approaches have been described by Esveld [4] and Zarembski [12]. Zhang [13] has proposed an Integrated Track Degradation Model (ITDM). ITDM simulates track degradation based on the interaction between different track components under varying traffic. It also considers several mechanistic characteristics, including train speed and axle load. Based on the ITDM, Simson et al. [11] have developed a Track Maintenance Planning Model (TMPM). It aims to deal with track maintenance planning in the medium to long term. TMPM outputs the net present value of the financial benefits of undertaking a given maintenance strategy compared with a base-case maintenance scenario. The Total Right-Of-Way Analysis and Costing System (TRACS) has been described by Martland et al. [7]. It is a system (software) developed by the Association of American Railroads (AAR) and Massachusetts Institute of Technology (MIT), in the USA. It is a computer-based tool developed to assist rail management to address change in the infrastructure. By combining engineering-based deterioration models with lifecycle costing techniques the model estimates track maintenance and renewal costs as a function of route geometry, track components, track condition, as well as traffic mix and volume. TRACS has been used by North American railroads as a tool for technology assessment costing in support of actions such as pricing, budgeting, and line consolidation. Both the ITDM and the TRACS models are based on an incremental approach where each event such as rail grinding, relining, and track renewal can be included. In recent years the application of soft computing techniques has been paid attention to predicting the future track conditions. Shafahi and Rasooli [10] have considered neuro-networks to predict future track conditions. A neuro-fuzzy decision support system for rail track maintenance planning has been described by Dell'Orco et al. [2]. During the 1990s, the International Railway Union (UIC) in conjunction with the European Rail Research Institute (ERRI) developed an expert system for track maintenance and renewal (ECOTRACK). This model builds on the fact that rules can be specified for certain maintenance activities under certain conditions. A historical database containing infrastructure information on components and current condition is also a prerequisite to use this model. ECOTRACK solves the planning problem given the rules specified and points out the activities needed at a section at certain time. Recent work on ECOTRACK at ERRI [6] has developed the model further in order to improve its functionality. To describe the condition of the track, several indexes and criteria have been defined and used in different railway systems around the world. Commonly, the track quality index (TQI) is used to define the track quality. TQI is normally determined by track geometry parameters (TGP). The term track deterioration is used to describe any changes in track geometry. Track deteriorations are classified as: unevenness, twist, alignment, and gauge. TQI is a function of these four parameters. In this study, the track’s state will be defined in terms of the Combined Track Record index (CTR index) rating which can vary from 0 to 100 where 100 denotes the best possible track condition and the states are defined as five intervals of CTR index (Table 1). It is supposed that the track began its life at some time in the past in near-perfect condition. It was then a subject to a sequence of duty cycles that caused its condition to deteriorate. The duty cycle for the track in this study will be assumed to consist of one year’s weather and traffic load. These discrete state and discrete time unit definition let us to express the deterioration process as a Markov chain. CTR index 0–50 50–60 60–70 70–80 80–100 Track quality Failed Medium Good Very good Excellent Track state 1 2 3 4 5 Table 1: Track state classification by CTR index To have better models, in this study the tracks are categorized so that those with similar traffic loads and geographical location are collected into one class. A network of tracks based on the topography was divided into three groups of plain, hilly and mountainous areas; and based on traffic loads was divided into two groups of light and heavy traffic. We thus have six track classes (Table 2). Traffic condition Topography condition Plain areas Hilly areas Mountainous areas