Norbert Pavlovic

A fuzzy Petri net model to estimate train delays

Abstract Even with the most accurate timetable, trains often operate with delays. The running and waiting times for trains can increase unexpectedly, creating primary delays that cause knock-on delays and delays for other trains. The accurate estimation of train delays is important for creating timetables, dispatching trains, and planning infrastructures. In this work, we proposed a fuzzy Petri net (FPN) model for estimating train delays. The FPN model with characteristics of hierarchy, colour, time, and fuzzy reasoning was used to simulate traffic processes and train movements in a railway system. The trains were coloured tokens , the track sections were termed places , and discrete events of train movement were termed transitions . The train primary delays were simulated by a fuzzy Petri net module in the model. The fuzzy logic system was incorporated in the FPN module in two ways. First, when there were no historical data on train delays, expert knowledge was used to define fuzzy sets and rules, transforming the expertise into a model to calculate train delays. Second, a model based on the Adaptive Network Fuzzy Inference System (ANFIS) was used for systems where the historical data on train delays were available (from detection systems or from the train dispatcher’s logs). The delay data were used to train the neuro-fuzzy ANFIS model. After the results of the fuzzy logic system were verified, the ANFIS model was replicated by a fuzzy Petri net. The simulation was validated by animating the train movement and plotting the time-distance graph of the trains. Results of the simulation were exported to a database for additional data mining and comparative analysis. The FPN model was tested on a part of the Belgrade railway node.

Analysis of impact of meteorological conditions on human factors in estimating the risk of railway accidents

AbstractThis paper explores the accidents that occurred in the railway system of Serbian Railways from 2006 to 2012. The total number of the sample observed includes 3983 accidents, of which 2725 or 68.41% were caused by the human factor. One of the major contemporary problems – global climate change and the increase of average temperatures – has not yet been considered in the context of external factors for increasing the risk of accidents. The air pressure has become accepted as an external factor, in addition to the air temperature. It is assumed that temperature and air pressure have a significant impact on the risk of railway accidents occurrence (taking only accidents caused by a human factor into account). This assumption was made based on reduced cognitive abilities of railway staff as a result of extreme differences in temperature and air pressure. In this paper, the emphasis is put on Railway Crossings (RC) as sites where two forms of traffic intersect, since it is noticed that certain weather c...

Fuzzy renewal theory about forecasting mistakes done by a locomotive driver: a Serbian railway case study

Abstract The human factor is one of the most dominant causes of railway accidents. For example, human impact appears to be the main reason for 44% of railway accidents in the Republic of Serbia. Thus, a remarkable effort is undertaken to investigate human factors. Therefore, plenty of researchers have analyzed a human influence on railway accidents. This paper develops a model for forecasting the number of railway accidents caused by the human factor. The proposed model is based on the renewal theory and assumes that working time between the faults of a locomotive driver has exponential distribution (or another Erlang distribution of a higher order) characterized by parameter λ that is treated as a fuzzy dependant variable and considered as a function of job complexity, the exposure of locomotive drivers (i.e. time spent in driving) and a tendency of locomotive drivers to make mistakes. The application of the model to the population of 777 Serbian railway locomotive drivers provided encouraging results in...