A multi-level clustering approach for forecasting taxi travel demand N Davis, G Raina, K Jagannathan 2016 IEEE 19th international conference on intelligent transportation …, 2016 | 80 | 2016 |
Taxi demand forecasting: A HEDGE-based tessellation strategy for improved accuracy N Davis, G Raina, K Jagannathan Transactions on Intelligent Transportation Systems 19 (11), 3686-3697, 2018 | 47 | 2018 |
A framework for end-to-end deep learning-based anomaly detection in transportation networks N Davis, G Raina, K Jagannathan Transportation research interdisciplinary perspectives 5, 100112, 2020 | 38 | 2020 |
Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts N Davis, G Raina, K Jagannathan IEEE Transactions on Intelligent Transportation Systems 22 (10), 6526-6535, 2020 | 36 | 2020 |
Congestion costs incurred on Indian Roads: A case study for New Delhi N Davis, HR Joseph, G Raina, K Jagannathan arXiv preprint arXiv:1708.08984, 2017 | 26 | 2017 |
LSTM-based anomaly detection: detection rules from extreme value theory N Davis, G Raina, K Jagannathan Progress in Artificial Intelligence: 19th EPIA Conference on Artificial …, 2019 | 7 | 2019 |
Taxi demand-supply forecasting: Impact of spatial partitioning on the performance of neural networks N Davis, G Raina, K Jagannathan arXiv preprint arXiv:1812.03699, 2018 | 3 | 2018 |
Demand modeling and forecasting in Taxi Hailing services N DAVIS Chennai, 0 | | |