Resurrection of “second order” models of traffic flow
SIAM Journal on Applied Mathematics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM SIGGRAPH Asia 2010 papers
Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatiotemporal Data
IEEE Transactions on Visualization and Computer Graphics
Simulating heterogeneous crowd behaviors using personality trait theory
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Interactive hybrid simulation of large-scale traffic
Proceedings of the 2011 SIGGRAPH Asia Conference
Detailed traffic animation for urban road networks
Graphical Models
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We present a video-based approach to learn the specific driving characteristics of drivers in the video for advanced traffic control. Each vehicle's specific driving characteristics are calculated with an offline learning process. Given each vehicle's initial status and the personalized parameters as input, our approach can vividly reproduce the traffic flow in the sample video with a high accuracy. The learned characteristics can also be applied to any agent-based traffic simulation systems. We then introduce a new traffic animation method that attempts to animate each vehicle with its real driving habits and show its adaptation to the surrounding traffic situation. Our results are compared to existing traffic animation methods to demonstrate the effectiveness of our presented approach.