TY - GEN
T1 - MultiCalib
T2 - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
AU - Zhang, Desheng
AU - Zhang, Fan
AU - He, Tian
N1 - Publisher Copyright:
© 2016 ACM.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-theart calibration by 25% on average with same input data.
AB - Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-theart calibration by 25% on average with same input data.
KW - Incomplete Data
KW - Model Calibration
UR - http://www.scopus.com/inward/record.url?scp=85011076306&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011076306&partnerID=8YFLogxK
U2 - 10.1145/2996913.2996918
DO - 10.1145/2996913.2996918
M3 - Conference contribution
AN - SCOPUS:85011076306
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
A2 - Renz, Matthias
A2 - Ali, Mohamed
A2 - Newsam, Shawn
A2 - Renz, Matthias
A2 - Ravada, Siva
A2 - Trajcevski, Goce
PB - Association for Computing Machinery
Y2 - 31 October 2016 through 3 November 2016
ER -