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iUTS welcomes new Ph.D. / MS Research Students: Ms. Shakiba Naderian, Mr. Soheil Kesharvarz, and Mr. Xin Wang. They come with strong background in transportation, AI/Computer Sciences, Statistics/Applied Math, and will add more to iUTS research, education, and outreach activities. Welcome them All!

iUTS Ph.D. student Qiangqiang Guo successfully defended his dissertation research on “Multiscale Signal-Vehicle Coupled Control with Connected and Autonomous Vehicles” on August 15, 2022. He has joined Google Map for his next journey. Congratulations to Dr. Guo!

Congratulations to iUTS/CEE 2022, 2021, 2020 graduates: Ph.D.: Jingxing Wang, Rong Fan, Qiangqiang Guo, Feilong Wang; MS: Iman Haji, Ebuka Umeibe, Sai Pavuluri, Hans Lu, Alex Lee, Michael Berlinger. A few MS graduates who could not attend the ceremony: Yanyan Chen, Ce Wang, Thomas Valdriz, Zhijun Liu, Ohay Angah, Dan McCabe. Congratulations to all and best luck with pursuing your future career & education!

Qiangqiang Guo, a fifth year Ph.D. student of iUTS, led a student team (Ohay Angah, Zhijun Liu) to win the First Place in the Student Research Competition on Artificial Intelligence Enabled Next Generation Transportation Systems, organized by the Artificial Intelligence in Transportation Committee of ASCE Transportation & Development Institute. His work, titled Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors, focuses on applying RL methods for AV control to save energy and improve safety. The paper has been published by Transportation Research Part C in 2021: https://www.sciencedirect.com/science/article/abs/pii/S0968090X21000164.

Congratulations to Qiangqiang and the team!

A new NSF grant was awarded to iUTS to develop new privacy protection methods for connected vehicles (CV) and V2X data. The award will be a joint collaboration between UW and privacy/security experts at IIT. See here for more detailed information about this award.

iUTS has conducted Covid-19 related research since early 2020 on (i) mobility impacts on multimodal transportation systems, (ii) surveys/focus groups on travel/commuting needs, challenges, and new patterns (especially for essential workers) during and post the pandemic, and (iii) mobility-based transmission models to help predict Covid fatalities. More details of the research can be found from the following media/news reports.

1. UW News (Jun. 01, 2021), Regional survey reveals work, leisure habits during the pandemic (https://www.washington.edu/news/2021/06/01/regional-survey-reveals-work-leisure-habits-during-the-pandemic/)
2. Mirage News (Jun. 02, 2021): Regional survey reveals work, leisure habits during the pandemic (https://www.miragenews.com/regional-survey-reveals-work-leisure-habits-569742/)   
3. MyScience (Jun. 02, 2021): Regional survey reveals work, leisure habits during the pandemic (https://www.myscience.org/en/news/2021/regional_survey_reveals_work_leisure_habits_during_the_pandemic-2021-washington/)  
4. UW CEE News (Jan. 12, 2021): A mobility informed COVID-19 transmission model (https://www.ce.washington.edu/news/article/2021-01-12/mobility-informed-covid-19-transmission-model); paper preprint: https://www.medrxiv.org/content/10.1101/2020.07.25.20162016v1
5. COVID-19 infection modeling and prediction, Times Union (https://www.timesunion.com/news/article/RPI-researcher-uses-simple-formula-for-chemical-15667043.php)
6. CDC on COVID-10 Forecasts: Death (October 21, 2020), RPI-UW Model is one of the models that contributes to the CDC Ensemble Model to forecast COVID-19 deaths: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html
7. RPI News (October 21, 2021): COVID-19 Model Inspired by Gas-Phase Chemistry Predicts Disease Spread: https://news.rpi.edu/content/2020/10/21/covid-19-model-inspired-gas-phase-chemistry-predicts-disease-spread
8. UW News (May 22, 2020): UW research team seeks campus input with survey on coronavirus mobility impacts:  https://www.washington.edu/news/2020/05/21/uw-research-team-seeks-campus-input-with-survey-on-coronavirus-mobility-impacts/?utm_source=UW_News_Subscribers&utm_medium=email&utm_campaign=UW_Today_row&mkt_tok=eyJpIjoiTldFeE9ETXdNekk1WWpZNCIsInQiOiJBSVpkK2JcL0Y5Z2dDR3Arall3T1llK0RmM2praWZjZzl5MjN1ckNnTTJnVVBjYkh6cmNcL3g1Vm5ncUEraGVQbEx0c3VVZDBIUHF4SlR6UTB1REV0WVdBdTE2NXN4Q3JCQm4xSFlhOXI0NUxielR4QVdlOHlKbEJheVpFRnJ6SWY0In0%3D
9. C2Smart, NYU (May 5, 2020): COVID-19 Transportation Data Dashboard and White Paper Issue 2: https://c2smart.engineering.nyu.edu/wp-content/uploads/2020/05/Whitepaper-Issue-2.pdf.

Ms. Ohay Angah and Mr. Feilong Wang  joined iUTS as Ph.D. students. Ohay is interested in multiscale transportation system simulation and control, and Feilong is interested in transportation big data and cybersecurity. Welcome to both Ohay and Feilong!

Dan McCabe was awarded by USDOT the 2021 Student of the Year of C2Smart University Transportation Center (UTC). Congratulations to Dan!

Members of iUTS Lab, Jingxing Wang, Rong Fan, Yiran Zhang, Qiangqiang Guo, Dan McCabe, and Jeff Ban attend the 2019 INFORMS Annual Meeting in Seattle (Oct. 20-23). Some of them presented their research results (see blow) and they all had a great time to interact with researchers who are interesting in applying analytics methods to transportation and many other engineering fields.

1. Wang, J., Lu, S., Ban, X., 2019. Exploring insignificant OD pairs: a compressed sensing model for OD demand estimation. Presented at the INFORMS Annual Meeting, Seattle, WA, Oct. 20-23, 2019.
2. Li, W., Ban. X., 2019. Deep learning methods for real time traffic volume prediction for signalized intersections. Presented at the INFORMS Annual Meeting, Seattle, WA, Oct. 20-23, 2019.
3. Guo, Q., Ban, X., 2019. Reinforced learning based traffic signal control. Presented at the INFORMS Annual Meeting, Seattle, WA, Oct. 20-23, 2019.
4. Zhang, Y., Guo, Q., Ban, X., 2019. Simulating CAVs on urban transportation networks. Presented at the INFORMS Annual Meeting, Seattle, WA, Oct. 20-23, 2019.

Mr. Dan McCabe joined our iUTS lab from the Pacific Northwest Nuclear Laboratory (PNNL). Dan is interested in applying optimization methods and techniques to transportation and electricity networks. Welcome Dan!