Paper Info
Title
Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data
Abstract
We present a novel concept, Virtualized Traffic, to reconstruct and visualize continuous traffic flows from discrete spatio-temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e. the dynamic motions of multiple cars over time) in between the two locations along the highway for immersive visualization of virtual cities or other environments. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. Our method reconstructs the car motion that automatically minimizes the number of lane changes, respects safety distance to other cars, and computes the acceleration necessary to obtain a smooth traffic flow subject to the given constraints. Furthermore, our framework can process a continuous stream of input data in real time, enabling the users to view virtualized traffic events in a virtual world as they occur.
Year
DOI
Venue
2009
10.1109/VR.2009.4811021
VR
Keywords
Field
DocType
data visualisation,data visualization,sensors,computer science,image reconstruction,robots,layout,real time,trajectory,virtual reality,traffic flow,mathematical model,kinematics,virtual worlds
Traffic generation model,Computer vision,Data visualization,Traffic wave,Traffic flow,Simulation,Computer science,Floating car data,Temporal database,Artificial intelligence,Traffic congestion reconstruction with Kerner's three-phase theory,Trajectory
Conference
Citations 
PageRank 
References 
19
1.58
14
Authors
4
Name
Order
Citations
PageRank
Jur van den Berg1197793.23
Jason Sewall241527.13
Ming Lin37046525.99
Dinesh Manocha49551787.40