journal article·2020-12-24Open access

Graphical Analysis of Rollback Process in Ad Hoc Distributed Traffic Simulation

Wonho Suh

Applied Sciences· Volume 11· Issue 1

Abstract

Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Simulation Techniques and Applications · Traffic control and management · Vehicular Ad Hoc Networks (VANETs)

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Smart City Engineering, ERICA Campus, Hanyang University, Ansan 15588, Korea

Funding

National Research Foundation of Korea

References (35)

Show source-provided references
1. Real-time data-driven traffic simulation for performance measure estimation10.1049/iet-its.2015.0155 ↗
2. Ad hoc distributed simulation for transportation system monitoring and near-term prediction10.1016/j.simpat.2013.11.002 ↗
3. Dynamic data driven transportation systems10.1007/s11042-016-4318-x ↗
4. Fujimoto, R. (2000). Parallel and Distributed Simulation Systems, Wiley-Interscience.
5. Distributed Database Semantic Integration of Wireless Sensor Network to Access the Environmental Monitoring System
6. Dual Polling Protocol for Improving Performance in Wireless Ad Hoc Networks
7. Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks
8. Fujimoto, R. (1999, January 5–8). Parallel and Distributed Simulation. Proceedings of the 1999 Winter Simulation Conference, Phoenix, AZ, USA.10.1145/324138.324176 ↗
9. Jefferson, D. (2020, December 24). Virtual Time. ACM Transactions on Programming Languages and Systems. Available online: http://cobweb.cs.uga.edu/~maria/pads/papers/p404-jefferson.pdf.
10. Fujimoto, R.M. (1989, January 4–6). Parallel Discrete Event Simulation. Proceedings of the 21st Conference on Winter, Washington, DC, USA.10.1145/76738.76741 ↗
11. Park, A., and Fujimoto, R. (2007, January 19–21). A scalable framework for parallel discrete event simulations on desktop grids. Proceedings of the 2007 8th IEEE/ACM International Conference on Grid Computing, Austin, TX, USA.10.1109/GRID.2007.4354132 ↗
12. Asynchronous parallel discrete event simulation10.1109/3468.508819 ↗
13. Lin, Y.B., Preiss, B., Loucks, W., and Lazowska, E. (1993, January 16–19). Selecting the Checkpoint Interval in Time Warp Simulation. Proceedings of the Seventh Workshop on Parallel and Distributed Simulation, San Diego, CA, USA.10.1145/158459.158460 ↗
14. Liu, H., Ma, W., Jayakrishnan, R., and Recker, W. (2004). Large-Scale Traffic Simulation Through Distributed Computing of Paramics, UC Berkeley: California Partners for Advanced Transportation Technology. PATH Re-Search Report.
15. Nagel, K., and Cetin, N. (2003). Parallel Queue Model Approach to Traffic Microsimulations, Transportation Research Board Annual Meeting.
16. Gendreau, M., and Marcotte, P. (2000). Real Time Simulation of Traffic Demand-Supply Interactions within DynaMIT. Transportation and Network Analysis: Current Trends, Springer.
17. Jayakrishnan, R., Sheu, P., Wang, T., and Xu, M. (2000). Database Environment for Fast Real-Time Simulation of Urban Traffic Networks with ATMIS, California Partners for Advanced Transit and Highways (PATH).
18. A Microscopic Traffic Simulator for evaluation of dynamic traffic management systems10.1016/S0968-090X(96)00006-X ↗
19. Parallel implementation of the TRANSIMS micro-simulation10.1016/S0167-8191(01)00106-5 ↗
20. Dynamic traffic assignment on parallel computers in TRANSIMS10.1016/S0167-739X(00)00032-7 ↗
21. Gu, Y. (2004). Integrating a Regional Planning Model (TRANSIMS) With an Operational Model (CORSIM). Civil Engineering, Virginia Polytechnic Institute and State University.
22. Marcotte, P., and Nguyen, S. (1998). Microscopic Traffic Simulation for ATT Systems Analysis—A Parallel Computing Version. Equilibrium and Advanced Transportation Modelling. Centre for Research on Transportation, Springer.10.1007/978-1-4615-5757-9 ↗
23. Algers, S., Bernauer, E., Boero, M., Breheret, L., Taranto, C., Dougherty, M., Fox, K., and Gabard, J. (2020, December 24). Review of Micro-Simulation Models. Available online: https://www.semanticscholar.org/paper/Review-of-MicroSimulation-Models-Algers-Bernauer/f981a04dbf276f4298bdaab3b5acf43af6fcea5f.
24. Cameron, G., Wylie, B.J.N., and McArthur, D. (2002, January 14–18). PARAMICS-moving vehicles on the connection machine. Proceedings of the 1994 ACM/IEEE Conference on Supercomputing—Supercomputing ’94, Washington, DC, USA.10.1145/602770.602826 ↗
25. PARAMICS—Parallel Microscopic Simulation of Road Traffic10.1007/BF00128098 ↗
26. A Framework for Parallel Traffic Simulation Using Multiple Instancing of a Simulation Program10.1080/713643721 ↗
27. Klefstad, R., Zhang, Y., Lai, M., Jayakrishnan, R., and Lavanya, R. (2005, January 13–16). A distributed, scalable, and synchronized framework for large-scale mi-croscopic traffic simulation. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Vienna, Austria.
28. MoVES: A framework for parallel and distributed simulation of wireless vehicular ad hoc networks10.1016/j.comnet.2007.09.015 ↗
29. Sippl, C., Schwab, B., Kielar, P.M., and Djanatliev, A. (2018, January 4–7). Distributed Real-Time Traffic Simulation for Autonomous Vehicle Testing in Urban Environments. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569544 ↗
30. Distributed Agent-Based Traffic Simulations10.1109/MITS.2017.2776162 ↗
31. Raghothama, J., and Meijer, S. (2015, January 6–9). Distributed, integrated and interactive traffic simulations. Proceedings of the 2015 Winter Simulation Conference (WSC), Huntington Beach, CA, USA.10.1109/WSC.2015.7408288 ↗
32. Chan, C., Wang, B., Bachan, J., and Macfarlane, J. (2018, January 4–7). Mobiliti: Scalable Transportation Simulation Using High-Performance Parallel Computing. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569397 ↗
33. Traffic Safety Evaluation Based on Vision and Signal Timing Data
34. Vision Technology Based Traffic Safety Analysis Using Signal Data
35. Mitigating Initialization Bias in Transportation Modeling Applications