Infrastructure-enabled Defense Solution against Data Poisoning Attacks on Queue Length Estimation using Mobile Sensors. Wang, F., Wang, X., Hong, Y., Ban, J., 2023. Working paper.

Data Poisoning Attacks on Traffic State Estimation and Prediction (TSEP). Wang, F., Wang, X., Hong, Y., Ban, J., 2023. Under review at ISTTT25. 

Are Big Mobility Data Reliable for Assessing the Performance of Transit Systems during COVID-19? Wang, F., Umeibe, E., Zhang, M., Ban, X., 2022. Under review at TRR. 

Modeling and Evaluating Impacts of Post-Covid Return-to-Work Plans on Transportation Systems. Wang, F., Semionov, A., Coe, S., Nichols, B., Lee, B., Simonson, M., Ban, X., 2022. Under review at TRR. 

Infrastructure-enabled GPS Spoofing Detection and Correction. Wang, F., Hong, Y., Ban, J., IEEE T-ITS. 2023. 

Data‐driven inference of interactions among multiple tourist attractions for hourly demand forecasting. Zhao, R., Wang, F., Zheng, W., 2022. Journal of Tourism Research. 

Extracting trips from multi-sourced data for individual mobility and travel pattern analysis: an app-based data example. F. Wang, J. Wang, C. Chen and X. Ban. Transportation Research Part C. 2019.

Personalized incentives for promoting sustainable travel behaviors. X. Zhu, F. Wang, C. Chen and D. Reed. Transportation Research Part C. 2019.

On data processing required to derive mobility patterns from passively-generated mobile phone data. F. Wang and C. Chen. Transportation Research Part C. 2018.

Characterizing preferred motif choices and distance impacts. J. Cao, Q. Li, W. Tu and F. Wang. PLoS ONE. 2019.

Percolation properties in a traffic model. F. Wang, D. Li, X. Xu, R. Wu and S. Havlin. Europhysics Letters. 2015.

Epidemics of interconnected small-world networks. M. Liu, D. Li, C. Liu, H. Wang and F. Wang. PLoS ONE. 2015. 

Assessing Impacts of Abnormal Events on Travel Patterns Leveraging Passively Collected Trajectory Data. F. Wang, X. Guan and C. Chen. arXiv:1911.11633. 2019.

Real-time Learning and Predicting Evacuation Behaviors from Big and Small Data. F. Wang, X. Guan and C. Chen. Prepared for submission. supp

Learning evacuation behaviors from transportation big data. F. Wang, X. Guan and C. Chen. Prepared for submission.

A Learning Framework for Personalized Random Utili-ty Maximization (RUM) Modeling of User Behavior. J. Feng, X Zhu, F Wang, S Huang and C. Chen. Submitted to IEEE Transactions on Automation Science and Engineering, 2020.

Promises of Data from Emerging Technologies for Trans-portation Applications: Puget Sound Region Case Study. X. Ban, C. Chen, F. Wang, J. Wang and Y. Zhang. Federal Highway Administration, 2018. 

Understanding GPS and Mobile Phone Data for Origin-Destination Analysis. C. Chen, X. Ban, F. Wang, J. Wang, C. Siddique, R. Fan and J. Lee. Federal Highway Administration, 2017. 

Data Poisoning Attacks and Infrastructure-Enabled Solutions for Traffic State Estimation and Prediction, 2019 – present

• Assisting NSF proposal writing; Preliminary analysis of vulnerability of common TSEP models;

• Developing an infrastructure-enabled solution to detection and correcting data poisoning attacks on vehicular data.

Scenario Modeling for Post-Covid Return to Work and Transit Usage, Challenge Seattle Association, 2020 – current.

• Data collection and processing; scenario building; modeling regional traffic and transit usage;

• Evaluating quality of big mobility data (e.g., Google data and Apple data);

• Analyzing and communicating findings with local agencies (e.g., City of Seattle, PSRC, WSDOT, King County

• Metro).

Promises of Data from Emerging Technologies for Transportation Applications, Federal Highway Administration (FHWA), 2018-Current. Details

• Mining real-world, noisy big datasets to address practical challenges; understanding properties of big data by comparing them against conventional sensor data; leveraging machine learning to predict travel patterns changes.

3-population 3-scale social network model to assess disease dispersion, National Institute of Health, 2015-Current. Details

• Data-driven modeling of human travel patterns in an entire region and evaluating their role in influenza spreading.

Real-time prediction of movement patterns during disaster times, Facebook research, 2018-2019. Details

• Leveraging a 3TB dataset to assess impacts of Hurricane Harvey on human trajectories in 100+ counties; inferring evacuation patterns of each person; extracting and assembling features from noisy data; learning and predicting evacuation behaviors via both machine learning and interpretable conventional models.

The connected traveler: a framework to reduce energy use in transportation, Department of Energy, 2015-2018. Details

• Developed a statistical model for learning travel behaviors and promoting personalized recommendations in real-time; developed an online experimental system for testing. 

Understanding GPS and Mobile Phone Data for Origin-Destination Analysis, FHWA, 2017. Details

• Comparing and understanding heterogeneous big data for estimating Origin-Destination travel demand.

Data modeling and analysis

• Stochastic Optimization; Stochastic Modeling of Scientific Data; Computational Methods for Data Analysis. 

Statistical learning

• Mathematical Statistics; Statistical Methods for Spatial Data; Statistical Learning. 

Machine learning and cloud computing

• Machine Learning for Big Data; Artificial Intelligence; Reinforcement Learning; Cloud Computing.

I play badminton, love runing, receive peace by walking dogs and get fun from cooking.