Sanjin Milinković

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.

Planning Dial-a-Ride Services: Statistical and Meta-Modeling Approach

Accessibility of public transit is an important political and social objective for transit agencies across the world. To meet this objective, many transit agencies provide a specialized door-to-door transportation service, called “dial-a-ride” (DAR), for the elderly and disabled. Annual DAR ridership growth exceeding 5% is reported in many cities in the United States, and this trend is expected to continue because of the aging population. In response to increased ridership, DAR services have become the fastest growing fraction of many transit agency budgets. These trends motivate the development of models that support decision making in the planning of new DAR systems or expansion of existing systems. Several statistical models have been developed in the past decade that can be used to determine the necessary DAR system capacity. These models focus on peak period analyses and provide good fit when applied to simulated case studies. This study aimed to demonstrate the importance of considering the entire day of operations rather than only the peak period. Several factors were identified that have been omitted in the literature, and comprehensive statistical and meta-models were developed for determining DAR system capacity. The performance of two proposed models was assessed with real-world data from a DAR service. The proposed models are available to the general public through a web system that provides free decision support to practitioners involved in designing DAR systems.

SOA architecture for complying with EU railway timetable data exchange format

When applying EU standards to the Serbian railway system, one of the major prerequisites is achieving interoperability with EU railway system. This paper describes a Web service for effortless timetable data exchange based on European Railway Agency implementation guide. The implementation details of the actual railway timetable formats and the corresponding software architecture are described, confirming advantages of the SOA approach when compared to other existing solutions in this area.