Flow reconstruction for data-driven traffic animation

Abstract

'Virtualized traffic' reconstructs and displays continuous traffic flows from discrete spatio-temporal traffic sensor data or procedurally generated control input to enhance a sense of immersion in a dynamic virtual environment. In this paper, we introduce a fast technique to reconstruct traffic flows from in-road sensor measurements or procedurally generated data for interactive 3D visual applications. Our algorithm estimates the full state of the traffic flow from sparse sensor measurements (or procedural input) using a statistical inference method and a continuum traffic model. This estimated state then drives an agent-based traffic simulator to produce a 3D animation of vehicle traffic that statistically matches the original traffic conditions. Unlike existing traffic simulation and animation techniques, our method produces a full 3D rendering of individual vehicles as part of continuous traffic flows given discrete spatio-temporal sensor measurements. Instead of using a color map to indicate traffic conditions, users could visualize and fly over the reconstructed traffic in real time over a large digital cityscape.

References

1. Aw, A., and Rascle, M. 2000. Resurrection of "second order" models of traffic flow. SIAM journal on applied mathematics, 916--938.
2. Cremer, J., Kearney, J., and Willemsen, P. 1997. Directable behavior models for virtual driving scenarios. Trans. Soc. Comput. Simul. Int. 14, 2, 87--96.
3. Cremer, M. 1991. Flow variables: estimation. Concise encyclopedia of traffic and transportation systems, 143--148.
4. Donikian, S., Moreau, G., and Thomas, G. 1999. Multimodal driving simulation in realistic urban environments. Progress in System and Robot Analysis and Control Design (LNCIS) 243, 321--332.
5. Evensen, G. 2003. The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean dynamics 53, 4, 343--367.
6. Hegyi, A., Mihaylova, L., Boel, R., and Lendek, Z. 2007. Parallelized particle filtering for freeway traffic state tracking. In Proc. European Control Conference.
7. Helbing, D. 2001. Traffic and related self-driven many-particle systems. Reviews of modern physics 73, 4, 1067.
8. Houtekamer, P., and Mitchell, H. 2001. A sequential ensemble kalman filter for atmospheric data assimilation. Monthly Weather Review 129, 2, 123--137.
9. Jacquet, D., Canudas De Wit, C., and Koenig, D. 2005. Traffic control and monitoring with a macroscopic model in the presence of strong congestion waves. In Proc. Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC'05. 44th IEEE Conference on, IEEE, 2164--2169.
10. Jacquet, D., Krstic, M., and de Wit, C. 2006. Optimal control of scalar one-dimensional conservation laws. In Proc. American Control Conference, 2006, IEEE, 6--pp.
11. Jia, Z., Chen, C., Coifman, B., and Varaiya, P. 2001. The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors. In Proc. IEEE Intelligent Transportation Systems, IEEE, 536--541.
12. Laramee, R. S., Hauser, H., Doleisch, H., Vrolijk, B., Post, F. H., and Weiskopf, D. 2004. The state of the art in flow visualization: Dense and texture-based techniques. Computer Graphics Forum 23, 2, 203--221.
13. Lebacque, J.-P., Mammar, S., and Haj-Salem, H. 2007. The aw-rascle and zhang's model: Vacuum problems, existence and regularity of the solutions of the riemann problem. Transportation Research Part B, 41, 710--721.
14. Lighthill, M., and Whitham, G. 1955. On kinematic waves. ii. a theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 229, 1178, 317--345.
15. 2011. MIT Intelligent Transportation Systems. http://mit.edu/its/mitsimlab.html.
16. 2013. Next Generation SIMulation. http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.
17. Papageorgiou, M., Blosseville, J., and Hadj-Salem, H. 1990. Modelling and real-time control of traffic flow on the southern part of boulevard périphérique in paris: Part i: Modelling. Transportation Research Part A: General 24, 5, 345--359.
18. Pausch, R., Crea, T., and Conway, M. 1992. A literature survey for virtual environments - military flight simulator visual systems and simulator sickness. Presence: Teleoperators and Virtual Environments 1, 3, 344--363.
19. Rice, J., and Van Zwet, E. 2004. A simple and effective method for predicting travel times on freeways. Intelligent Transportation Systems, IEEE Transactions on 5, 3, 200--207.
20. Richards, P. 1956. Shock waves on the highway. Operations research, 42--51.
21. Sau, J., El Faouzi, N., Aissa, A., and de Mouzon, O. 2007. Particle filter-based real-time estimation and prediction of traffic conditions. Recent advances in stochastic modeling and data analysis: Chania, Greece, 29 May-1 June 2007, 406.
22. Sewall, J., Wilkie, D., Merrell, P., and Lin, M. 2010. Continuum traffic simulation. In Computer Graphics Forum, vol. 29, Wiley Online Library, 439--448.
23. Sewall, J., van den Berg, J., Lin, M., and Manocha, D. 2011. Virtualized traffic: Reconstructing traffic flows from discrete spatiotemporal data. Visualization and Computer Graphics, IEEE Transactions on 17, 1, 26--37.
24. Sewall, J., Wilkie, D., and Lin, M. C. 2011. Interactive hybrid simulation of large-scale traffic. ACM Transaction on Graphics (Proceedings of SIGGRAPH Asia) 30, 6 (December).
25. Sewall, J. 2011. Efficient, Scalable Traffic and Compressible Fluid Simulations Using Hyperbolic Models. PhD thesis, University of North Carolina at Chapel Hill.
26. Thomas, G., and Donikian, S. 2000. Modelling virtual cities dedicated to behavioural animation. Computer Graphics Forum 19, 3.
27. Treiber, M., Hennecke, A., and Helbing, D. 2000. Congested traffic states in empirical observations and microscopic simulations. Physical Review E 62, 2, 1805--1824.
28. van den Berg, J., Sewall, J., Lin, M., and Manocha, D. 2009. Virtualized traffic: Reconstructing traffic flows from discrete spatio-temporal data. In Proc. IEEE Virtual Reality Conference., IEEE, 183--190.
29. Wang, Y., and Papageorgiou, M. 2005. Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transportation Research Part B: Methodological 39, 2, 141--167.
30. Wang, H., Kearney, J., Cremer, J., and Willemsen, P. 2005. Steering behaviors for autonomous vehicles in virtual environments. In Proc. IEEE Virtual Reality Conf., 155--162.
31. Wilkie, D., van den Berg, J., Lin, M., and Manocha, D. 2011. Self-Aware traffic route planning. Proc. of AAAI 2011.
32. Wilkie, D., Sewall, J., and Lin, M. 2012. Transforming GIS data into functional road models for large-scale traffic simulation. IEEE Trans. on Visualization and Computer Graphics 18, 6.
33. Work, D., Tossavainen, O., Blandin, S., Bayen, A., Iwuchukwu, T., and Tracton, K. 2008. An ensemble kalman filtering approach to highway traffic estimation using gps enabled mobile devices. In Proc. IEEE Decision and Control, IEEE, 5062--5068.
34. Work, D., Blandin, S., Tossavainen, O., Piccoli, B., and Bayen, A. 2010. A traffic model for velocity data assimilation. Applied Mathematics Research eXpress.
35. Zhang, H. 2002. A non-equilibrium traffic model devoid of gaslike behavior. Transportation Research Part B: Methodological 36, 3, 275--290.

Index Terms

• Computing methodologies

  • Computer graphics

    • Animation

• Mathematics of computing

  • Mathematical analysis

    • Numerical analysis

      • Interpolation