TY - GEN
T1 - CIKM 2019 workshop on artificial intelligence in transportation
AU - Zhang, Weinan
AU - Jin, Haiming
AU - Zhang, Lingyu
AU - Zhu, Hongtu
AU - Li, Jessie Zhenhui
AU - Ye, Jieping
N1 - Funding Information:
Dr. Haiming Jin is currently an assistant professor of the John Hopcroft Center for Computer Science at Shanghai Jiao Tong University. His major research interests include Urban Computing, Smart City, Crowd Sensing, Cyber-Physical Systems, and Internet of Things. He obtained his Ph.D. degree from the Department of Computer Science at the University of Illinois at Urbana-Champaign, advised by Professor Klara Nahrstedt. His research results have been broadly published in a series of high-quality international journals and conferences, such as IEEE/ACM TON, IEEE TMC, Mo-biHoc, INFOCOM. He served on the TPC of ICDCS 2019, IPSN 2016, and WiOpt 2018, and served as the TPC Chair of the poster program of IoTDI 2019. He is the winner of several research awards, including the CCF-DIDI GAIA Young Scholar Research Award, and the Dean’s Fellowship offered by the Engineering School of UIUC from 2017 to 2018.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/3
Y1 - 2019/11/3
N2 - Data-enabled smart transportation has attracted a surge of interest from machine learning and data mining researchers nowadays due to the bloom of online ride-hailing industry and rapid development of autonomous driving. Large-scale high quality route data and trading data (spatiotemporal data) have been generated every day, which makes AI an urgent need and preferred solution for the decision making in intelligent transportation systems. While a large of amount of work have been dedicated to traditional transportation problems, they are far from satisfactory for the rising need. We propose a half-day workshop at CIKM 2019 for the professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation, and AI applications to improve the transportation system. We plan to have several invited talks from both academia and industry. This workshop would be organized by Shanghai Jiao Tong University, Didi Chuxing and Pennsylvania State University.
AB - Data-enabled smart transportation has attracted a surge of interest from machine learning and data mining researchers nowadays due to the bloom of online ride-hailing industry and rapid development of autonomous driving. Large-scale high quality route data and trading data (spatiotemporal data) have been generated every day, which makes AI an urgent need and preferred solution for the decision making in intelligent transportation systems. While a large of amount of work have been dedicated to traditional transportation problems, they are far from satisfactory for the rising need. We propose a half-day workshop at CIKM 2019 for the professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation, and AI applications to improve the transportation system. We plan to have several invited talks from both academia and industry. This workshop would be organized by Shanghai Jiao Tong University, Didi Chuxing and Pennsylvania State University.
UR - http://www.scopus.com/inward/record.url?scp=85075426032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075426032&partnerID=8YFLogxK
U2 - 10.1145/3357384.3358802
DO - 10.1145/3357384.3358802
M3 - Conference contribution
AN - SCOPUS:85075426032
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2995
EP - 2996
BT - CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Y2 - 3 November 2019 through 7 November 2019
ER -