Dependability analysis of the data communication system in train control system

Communication based train control (CBTC) system is based on mobile communication and overcomes fixed blocks in order to increase track utilization and train safety. The data communication system (DCS) between trains and wayside equipment is a crucial factor for the safe and efficient operation of CBTC system. The dependability under various transmission conditions needs to be modeled and evaluated. In this paper, a stochastic reward net (SRN) model for DCS based IEEE 802.11 standard was developed, which captures all relevant failure and failure recovery behavior system aspects in a concise way. We compared the reliability, availability for DCS with and without access point (AP) and antenna redundant configuration. We also quantitatively evaluated and compared the frame loss probability for three DCS configurations with different train velocities and train numbers in one radio cell. Fixed-point iteration was adopted to simplify the analysis. Numerical results showed the significant improvement of the reliability, availability and the frame loss probability index for the full redundant configuration.

[1]  Holger Hermanns,et al.  From StoCharts to MoDeST: a comparative reliability analysis of train radio communications , 2005, WOSP '05.

[2]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[3]  Gao Zi-You,et al.  Modeling and simulation for train control system using cellular automata , 2007 .

[4]  James Lyle Peterson,et al.  Petri net theory and the modeling of systems , 1981 .

[5]  Günter Hommel,et al.  Towards modeling and evaluation of ETCS real-time communication and operation , 2005, Journal of Systems and Software.

[6]  Paolo Traverso,et al.  Formal Specification and Development of a Safety-Critical Train Management System , 1999, SAFECOMP.

[7]  Reinhard German,et al.  Performance modeling of IEEE 802.11 wireless LANs with stochastic Petri nets , 2001, Perform. Evaluation.

[8]  Peter G. Neumann,et al.  Reliability and Security , 1995 .

[9]  S. Anderson,et al.  Secure Synthesis of Code: A Process Improvement Experiment , 1999, World Congress on Formal Methods.

[10]  Erhan Çinlar,et al.  Introduction to stochastic processes , 1974 .

[11]  Werner Damm,et al.  Verification of a Radio-Based Signaling System Using the STATEMATE Verification Environment , 2001, Formal Methods Syst. Des..

[12]  F. Whitwom Integration of wireless network technology with signaling in the rail transit industry , 2003 .

[13]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[14]  Kishor S. Trivedi,et al.  Automated Generation and Analysis of Markov Reward Models Using Stochastic Reward Nets , 1993 .

[15]  William H. Sanders,et al.  Möbius: an integrated discrete-event modeling environment , 2007, Bioinform..

[16]  Kishor S. Trivedi,et al.  Composite Performance and Dependability Analysis , 1992, Perform. Evaluation.

[17]  Kishor S. Trivedi,et al.  SPNP: Stochastic Petri Nets. Version 6.0 , 2000, Computer Performance Evaluation / TOOLS.

[18]  Eckehard Schnieder,et al.  Formal Modelling and Simulation of Train Control Systems Using Petri Nets , 1999, World Congress on Formal Methods.