Ad Hoc Distributed Dynamic Data-Driven Simulations of Surface Transportation Systems

An ad hoc distributed dynamic data-driven simulation is a collection of autonomous online simulations brought together to model an operational system. They offer the potential of increased accuracy, responsiveness, and robustness compared to centralized approaches. They differ from conventional distributed simulations in that they are created bottom-up rather than top-down. They combine concepts from conventional distributed simulations and replicated trials, raising new issues in data management and synchronization. In this article, the ad hoc simulation approach and an optimistic synchronization algorithm are proposed. A prototype coupling in-vehicle transportation simulation is evaluated and shown to yield results comparable to a traditional replicated experiment for the tested scenarios. Experiences applying this concept to a commercial transportation simulator in an emergency scenario are described.

[1]  Hajime Kita,et al.  Area-wide Control of Traffic Signals by Phase Model , 2003 .

[2]  Frederica Darema,et al.  Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements , 2004, International Conference on Computational Science.

[3]  Satu Innamaa,et al.  ONLINE TRAFFIC MODELS - A LEARNING EXPERIENCE , 2004 .

[4]  T. Kanade,et al.  Monte Carlo road safety reasoning , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[5]  M. Schreckenberg,et al.  Microscopic Simulation of Urban Traffic Based on Cellular Automata , 1997 .

[6]  Richard M. Fujimoto,et al.  The virtual time machine , 1989, SPAA '89.

[7]  Houbing Song,et al.  DynaCHINA: Specially-Built Real-Time Traffic Prediction System for China , 2007 .

[8]  Paul F. Reynolds,et al.  Semi-automated Simulation Transformation for DDDAS , 2005, International Conference on Computational Science.

[9]  David R. Jefferson,et al.  Virtual time , 1985, ICPP.

[10]  Der-Horng Lee,et al.  Dynamic Routing Decisions for Commercial Vehicle Operations in Real-Time Traffic Conditions , 2004 .

[11]  J. Bohatkiewicz,et al.  Dynamic traffic management systems on A4 motorways , 2010 .

[12]  Sara J. Graves,et al.  Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD , 2005, International Conference on Computational Science.

[13]  Georgios Theodoropoulos,et al.  Intelligent Management of Data Driven Simulations to Support Model Building in the Social Sciences , 2006, International Conference on Computational Science.

[14]  Leon F. McGinnis,et al.  An integrated and adaptive decision-support framework for high-tech manufacturing and service networks , 2005, Proceedings of the Winter Simulation Conference, 2005..

[15]  Philip A. Wilsey,et al.  Unsynchronized parallel discrete event simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[16]  Roland Chrobok,et al.  A High‐Resolution Cellular Automata Traffic Simulation Model with Application in a Freeway Traffic Information System , 2004 .

[17]  Richard M. Fujimoto,et al.  Exploiting temporal uncertainty in parallel and distributed simulations , 1999, Proceedings Thirteenth Workshop on Parallel and Distributed Simulation. PADS 99. (Cat. No.PR00155).

[18]  Brian Lee Smith,et al.  Online implementation of DynaMIT: A prototype traffic estimation and prediction program , 2007 .

[19]  Leon F. McGinnis,et al.  An integrated and adaptive decision-support framework for high-tech manufacturing and service networks , 2005 .

[20]  Gaurav S. Sukhatme,et al.  A Generic Multi-scale Modeling Framework for Reactive Observing Systems: An Overview , 2006, International Conference on Computational Science.

[21]  Guan Qin,et al.  Towards a Dynamic Data Driven Application System for Wildfire Simulation , 2005, International Conference on Computational Science.