Resurrection of “second order” models of traffic flow
SIAM Journal on Applied Mathematics
Paramics: moving vehicles on the connection machine
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatiotemporal Data
IEEE Transactions on Visualization and Computer Graphics
Video-based personalized traffic learning
Graphical Models
| Hi-index | 0.00 |
We present a new agent-based system for detailed traffic animation on urban arterial networks with diverse junctions like signalized crossing, merging and weaving areas. To control the motion of traffic for visualization and animation purposes, we utilize the popular follow-the-leader method to simulate various vehicle types and intelligent driving styles. We also introduce a continuous lane-changing model to imitate the vehicle's decision-making process and dynamic interactions with neighboring vehicles. By applying our approach in several typical urban traffic scenarios, we demonstrate that our system can well visualize vehicles' behaviors in a realistic manner on complex road networks and generate immersive traffic flow animations with smooth accelerating strategies and flexible lane changes.