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

[王征(大数据技术部)]

**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://illidanlab.github.io/big_traffic/2018/index.html<https://urldefense.proofpoint.com/v2/url?u=https-3A__illidanlab.github.io_big-5Ftraffic_2018_index.html&d=DwMFaQ&c=nE__W8dFE-shTxStwXtp0A&r=Mm8XOes7f-8BuNbzowT-cIJlrkvFw31wSTHtjeJInMw&m=7tG61l7WxnR3czFcK2Adc6lqiq3oKR_FTq07LrKzIuU&s=H1VhakzYD5mmTvHNABhnR-JXyaGpW9jKTCoHfAfyuyI&e=>
Submission link https://easychair.org/conferences/?conf=bigtraffic2018<https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dbigtraffic2018&d=DwMFaQ&c=nE__W8dFE-shTxStwXtp0A&r=Mm8XOes7f-8BuNbzowT-cIJlrkvFw31wSTHtjeJInMw&m=7tG61l7WxnR3czFcK2Adc6lqiq3oKR_FTq07LrKzIuU&s=KzHfFrjL1vHB2st848tASlUBaLXDN0oPWSyiCq9-Gtk&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<mailto:jiayuz at msu.edu>
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