Abstract
Microscopic traffic simulation is one of the effective tools for transportation forecast and decision support. It is a challenge task to make reasonable prediction of traffic scenarios during emergency. Big data technology provides a new solution for this issue. This paper proposes a cross-simulation method to apply the mass data collected in normal situations into large-scale traffic evacuations to provide better supporting information for emergency decision. The method consists of three processes: Acquisition, Analysis and Adaptation. It captures the dynamic distance-speed relation of every vehicles on the real roads and build a database of driving behaviors according to the existing car-following models. After calibration and analysis, various driving behaviors can be identified. During emergency, the distribution of driving behaviors will be refactored to adapt the fast-changing situation automatically so that the simulation system gains the adaptive ability in emergency situations. An experimental result on a real road preliminarily validates the practicability of the method and shows the supporting information which it can provide. The new method will make contributions on enhancing the predictive ability of traffic simulation systems in emergency situations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dia, H.: An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transp. Res. Part C Emer. Technol. 10(5), 331–349 (2002)
Tawfik, A.M., Rakha, H.A., Miller, S.D.: Driver route choice behavior: Experiences, perceptions, and choices. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 1195–1200. IEEE (2010)
Treiber, M., Helbing, D.: Memory effects in microscopic traffic models and wide scattering in flow-density data. Phys. Rev. E 68(4), 046119 (2003)
Parker, D., Lajunen, T., Summala, H.: Anger and aggression among drivers in three European countries. Accid. Anal. Prev. 34(2), 229–235 (2002)
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108. ACM (2010)
Tu, H., Tamminga, G., Drolenga, H., de Wit, J., van der Berg, W.: Evacuation plan of the city of Almere: simulating the impact of driving behavior on evacuation clearance time. Procedia Eng. 3, 67–75 (2010)
Brackstone, M., McDonald, M.: Car-following: a historical review. Transp. Res. Part F Traffic Psychol. Behav. 2(4), 181–196 (1999)
Gipps, Parker, D., Lajunen, T., Summala, H., 2002. Anger and aggression among drivers in three European countries. Accident Analysis & Prevention, 34(2), 229-235
Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (No. 91324022, No.91224008, No.91024032, No.70601015, No.70833003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yuan, S., Liu, Y., Wang, G., Zhang, H. (2014). A Cross-Simulation Method for Large-Scale Traffic Evacuation with Big Data. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-11538-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11537-5
Online ISBN: 978-3-319-11538-2
eBook Packages: Computer ScienceComputer Science (R0)