| Factors generating boardings at Metro stations in the Seoul metropolitan area K Sohn, H Shim Cities 27 (5), 358-368, 2010 | 216 | 2010 |
| Classification of crystal structure using a convolutional neural network WB Park, J Chung, J Jung, K Sohn, SP Singh, M Pyo, N Shin, KS Sohn IUCrJ 4 (4), 486-494, 2017 | 196 | 2017 |
| An analysis of Metro ridership at the station-to-station level in Seoul J Choi, YJ Lee, T Kim, K Sohn Transportation 39, 705-722, 2012 | 177 | 2012 |
| Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN) B Yu, Y Lee, K Sohn Transportation research part C: emerging technologies 114, 189-204, 2020 | 169 | 2020 |
| Dynamic origin–destination flow estimation using cellular communication system K Sohn, D Kim IEEE Transactions on Vehicular Technology 57 (5), 2703-2713, 2008 | 141 | 2008 |
| Image-based learning to measure traffic density using a deep convolutional neural network J Chung, K Sohn IEEE Transactions on Intelligent Transportation Systems 19 (5), 1670-1675, 2017 | 110 | 2017 |
| Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model G Han, K Sohn Transportation Research Part B: Methodological 83, 121-135, 2016 | 101 | 2016 |
| Why do passengers choose a specific car of a metro train during the morning peak hours? H Kim, S Kwon, SK Wu, K Sohn Transportation research part A: policy and practice 61, 249-258, 2014 | 82 | 2014 |
| Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data J Jung, K Sohn IET Intelligent Transport Systems 11 (6), 334-339, 2017 | 80 | 2017 |
| Space-based passing time estimation on a freeway using cell phones as traffic probes K Sohn, K Hwang IEEE Transactions on Intelligent Transportation Systems 9 (3), 559-568, 2008 | 66 | 2008 |
| An extremely simple macroscale electronic skin realized by deep machine learning. KS Sohn, J Chung, MY Cho, S Timilsina, WB Park, M Pyo, N Shin, K Sohn, ... Scientific reports 7 (1), 11061, 2017 | 60 | 2017 |
| Separation of car-dependent commuters from normal-choice riders in mode-choice analysis K Sohn, J Yun Transportation 36, 423-436, 2009 | 60 | 2009 |
| Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation M Ko, T Kim, K Sohn Transportation 40, 91-107, 2013 | 57 | 2013 |
| Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies D Jo, B Yu, H Jeon, K Sohn IEEE Transactions on Vehicular Technology 68 (2), 1188-1197, 2018 | 52 | 2018 |
| Multi-objective optimization of a road diet network design K Sohn Transportation research part A: policy and practice 45 (6), 499-511, 2011 | 52 | 2011 |
| Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation M Lee, K Sohn Transportation Research Part B: Methodological 81, 1-17, 2015 | 47 | 2015 |
| Analyzing the time frame for the transition from leisure-cyclist to commuter-cyclist H Park, YJ Lee, HC Shin, K Sohn Transportation 38, 305-319, 2011 | 43 | 2011 |
| Artificial intelligence for traffic signal control based solely on video images H Jeon, J Lee, K Sohn Journal of intelligent transportation systems 22 (5), 433-445, 2018 | 41 | 2018 |
| Identifying driver heterogeneity in car-following based on a random coefficient model I Kim, T Kim, K Sohn Transportation research part C: emerging technologies 36, 35-44, 2013 | 41 | 2013 |
| Reinforcement learning for joint control of traffic signals in a transportation network J Lee, J Chung, K Sohn IEEE Transactions on Vehicular Technology 69 (2), 1375-1387, 2019 | 38 | 2019 |
