Call for Papers: 1st Int'l Workshop on Big Traffic Data Analytics (BigTraffic 2018)

[Jiayu Zhou]

**apologies for cross-posting**

*BigTraffic 2018: 1st Int'l Workshop on Big Traffic Data Analytics*
Venue: San Diego Marriott Mission Valley
San Diego, CA, United States, May 5, 2018

Conference website https://urldefense.proofpoint.com/v2/url?u=https-3A__illidanlab.github.io_big-5Ftraffic_2018_index.html&d=DwIBaQ&c=nE__W8dFE-shTxStwXtp0A&r=QWfjqZrx6cy8BF2ZHAGwcrFgMz68fN_wFM_P-xO4WV8&m=WOymTyOTUswTys87Kkm_8lvjrKsHqKt2slAop91IrhY&s=EfyN2ePxo9PF814jER8BnK50GH6d99bB37Tg43e_Zx4&e=
Submission link https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dbigtraffic2018&d=DwIBaQ&c=nE__W8dFE-shTxStwXtp0A&r=QWfjqZrx6cy8BF2ZHAGwcrFgMz68fN_wFM_P-xO4WV8&m=WOymTyOTUswTys87Kkm_8lvjrKsHqKt2slAop91IrhY&s=k2YJSDs3ubLCt-tYpwMto2_BIRF_lA8DvbK42mE-Hzc&e=

Submission deadline *January 19, 2018*

The BigTraffic workshop, in conjunction with 18th SIAM International
Conference on Data Mining (SDM 2018), aims to bring the attention of
researchers to the various data mining and machine learning methods for
traffic studies, and therefore promote AI research. The availability of
massive amount of travel data has provided unique opportunities for
data-driven intelligent transportation systems. Even though traffic
patterns are extensively studied from both the marcoscopic traffic level
(e.g., urban traffic patterns) and microscropic traffic level (e.g.,
behavior of individual drivers/vehicles), the small-scale data used in
prior studies has greatly restricted the complexity of models and thus the
capability of capturing complicated dynamics in traffic patterns. The
availability of big traffic data has enabled a wide spectrum of powerful
machine learning and data mining methodologies to be applied to traffic
studies.

We encourage submissions on a variety of topics, including but not limited
to:
1. Novel machine learning algorithms for traffic studies, e.g., new
trajectory analysis algorithms, new traffic state estimation algorithms,
new data-driven algorithms for map matching.
2. Novel approaches for applying existing machine learning algorithms,
e.g., applying bilinear models, sparse learning, metric learning, neural
networks and deep learning, for traffic studies.
3. Novel optimization algorithms and analysis for improving traffic data
processing, e.g., parallel/distributed optimization techniques and
efficient stochastic gradient descent.
4. Industrial practices and implementations of big traffic data modeling,
e.g., feature engineering, model ensemble, and lessons from large-scale
implementations of intelligent transportation systems.

> *Submission Guidelines*
The workshop accepts long paper and short (demo/poster) papers. Short
papers submitted to this workshop should be limited to 4 pages while long
papers should be limited to 8 pages. All papers should be formatted using
the SIAM SODA macro.

> *Invited Speakers*
Yan Liu, University of Southern California
[More speakers will be confirmed soon]

> *Program Committee*
Zhenhui Li, Pennsylvania State University
Shiyu Chang, IBM Research
Yanjie Fu, Missouri University of Science and Technology
Yong Ge, University of Arizona
Guannan Liu, Beihang University
Jiawei Zhang, Florida State University
Zijun Yao, Rutgers University
Jianpeng Xu, eBay Inc.
Defu Lian, University of Electronic Science and Technology of China

> *Organizing committee*
Jiayu Zhou, Michigan State University
Zheng Wang, Didi Research
Jieping Ye, Didi Research

> *Contact*
All questions about submissions should be emailed to jiayuz at msu.edu
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