| Predictor fusion for short-term traffic forecasting F Guo, JW Polak, R Krishnan Transportation Research Part C 92 (2018), 90-100, 2018 | 87 | 2018 |
| Transferability improvement in short-term traffic prediction using stacked LSTM network J Li, F Guo, A Sivakumar, Y Dong, R Krishnan Transportation Research Part C: Emerging Technologies 124, 102977, 2021 | 78 | 2021 |
| Short-term prediction of traffic flow using a binary neural network VJ Hodge, R Krishnan, J Austin, J Polak, T Jackson Neural Computing and Applications 25, 1639-1655, 2014 | 78 | 2014 |
| Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles T Li, F Guo, R Krishnan, A Sivakumar, J Polak Transportation research part C: emerging technologies 115, 102630, 2020 | 65 | 2020 |
| A computationally efficient two-stage method for short-term traffic prediction on urban roads F Guo, R Krishnan, J Polak Transportation planning and technology 36 (1), 62-75, 2013 | 64 | 2013 |
| Reliability of bluetooth technology for travel time estimation BN Araghi, J Hammershøj Olesen, R Krishnan, L Tørholm Christensen, ... Journal of Intelligent Transportation Systems 19 (3), 240-255, 2015 | 57 | 2015 |
| A Deep Learning Approach for Traffic Incident Detection in Urban Networks L Zhu, F Guo, R Krishnan, JW Polak 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018 | 54 | 2018 |
| Accuracy of travel time estimation using Bluetooth technology: Case study Limfjord tunnel Aalborg B Namaki Araghi, K Skoven Pedersen, L Tørholm Christensen, ... International Journal of Intelligent Transportation Systems Research 13, 166-191, 2015 | 54 | 2015 |
| Application of Bluetooth technology for mode-specific travel time estimation on arterial roads: Potentials and challenges BN Araghi, LT Christensen, R Krishnan, H Lahrmann Proceedings from the Annual Transport Conference at Aalborg University 19 (1), 2012 | 44 | 2012 |
| Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method F Guo, R Krishnan, JW Polak IET Digital Library, 2012 | 41 | 2012 |
| Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions F Guo, JW Polak, R Krishnan 13th International IEEE Conference on Intelligent Transportation Systems …, 2010 | 40 | 2010 |
| On the estimation of space-mean-speed from inductive loop detector data J Han, JW Polak, J Barria, R Krishnan Transportation planning and technology 33 (1), 91-104, 2010 | 39 | 2010 |
| Urban link travel time estimation using traffic states‐based data fusion L Zhu, F Guo, JW Polak, R Krishnan IET Intelligent Transport Systems 12 (7), 651-663, 2018 | 37 | 2018 |
| Mode-specific travel time estimation using Bluetooth technology B Namaki Araghi, R Krishnan, H Lahrmann Journal of Intelligent Transportation Systems 20 (3), 219-228, 2016 | 34 | 2016 |
| Short-term traffic prediction under normal and abnormal traffic conditions on urban roads F Guo, R Krishnan, JW Polak Transportation Research Board 91st Annual Meeting 12, 1627, 2012 | 22 | 2012 |
| Traffic Monitoring and Anomaly Detection based on Simulation of Luxembourg Road Network L Zhu, R Krishnan, A Sivakumar, F Guo, JW Polak IEEE Intelligent Transportation Systems Conference - ITSC 2019, 2019 | 21 | 2019 |
| A comparative study of k-NN and hazard-based models for incident duration prediction BN Araghi, S Hu, R Krishnan, M Bell, W Ochieng 2014 IEEE 17th International Conference on Intelligent Transportation …, 2014 | 21 | 2014 |
| The use of convolutional neural networks for traffic incident detection at a network level L Zhu, F Guo, R Krishnan, JW Polak Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018 | 20 | 2018 |
| Short-term travel time prediction: An overview of methods and recurring themes R Krishnan, J Polak Transportation Planning and Implementation Methodologies for Developing …, 2008 | 16 | 2008 |
| The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework F Guo, R Krishnan, J Polak Journal of Intelligent Transportation Systems 21 (3), 214-226, 2017 | 15 | 2017 |
John PolakProfessor of Transport Demand, Imperial College London在 imperial.ac.uk 的电子邮件经过验证
