Taehooie Kim

A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service

Taehooie Kim, Shivam Sharda, Xuesong Zhou et al. · Transportation Research Part C Emerging Technologies · 2020 · 81 citations

Transportation Planning, Engineering, Machine Learning +11

Fusing Multiple Sources of Data to Understand Ride-Hailing Use

Felipe F. Dias, Patrícia Sauri Lavieri, Taehooie Kim et al. · Transportation Research Record Journal of the Transportation Research Board · 2019 · 54 citations

Fused Data, Engineering, Activity-travel Pattern +20

Congestion-Aware Cooperative Adaptive Cruise Control for Mitigation of Self-Organized Traffic Jams

Taehooie Kim, Kshitij Jerath · IEEE Transactions on Intelligent Transportation Systems · 2021 · 28 citations

Adaptive Cruise Control, Intelligent Traffic Management, Engineering +9

Computational graph-based framework for integrating econometric models and machine learning algorithms in emerging data-driven analytical environments

Taehooie Kim, Xuesong Zhou, Ram M. Pendyala · Transportmetrica A Transport Science · 2021 · 23 citations

Engineering, Machine Learning Algorithms, Computational Graph +26

Modeling the Evolution of Ride-Hailing Adoption and Usage: A Case Study of the Puget Sound Region

Felipe F. Dias, Taehooie Kim, Chandra R. Bhat et al. · Transportation Research Record Journal of the Transportation Research Board · 2020 · 15 citations · Full text

Consumer Research, Ride-hailing Use, On-demand Transport +21