- Quantifying the impacts of non-recurrent congestion on workplace EV charging infrastructures
Kumar, N., Wang, Y., Chin, J.-X., & Raubal, M. (2025)
Transportation Research Part D: Transport and Environment, 146, 104869.
https://doi.org/10.1016/j.trd.2025.104869 - Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis
Kumar, N., Martin, H., & Raubal, M. (2024)
Data Science for Transportation, 6(3), Article 24.
https://doi.org/10.1007/s42421-024-00109-x - Applications of Deep Learning in Congestion Detection, Prediction and Alleviation: A Survey
Kumar, N., & Raubal, M. (2021)
Transportation Research Part C: Emerging Technologies, 133, 103432.
https://doi.org/10.1016/j.trc.2021.103432 - Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas
Kumar, N., Oke, J. B., & Nahmias-Biran, B.-H. (2021)
Scientific Reports, 11, 22665.
https://doi.org/10.1038/s41598-021-01522-w - Who benefits from AVs? Equity implications of automated vehicles policies in full-scale prototype cities
Nahmias-Biran, B.-H., Oke, J. B., & Kumar, N. (2021)
Transportation Research Part A: Policy and Practice, 154, 92-107.
https://doi.org/10.1016/j.tra.2021.09.013 - Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach
Nahmias-Biran, B.-H., Oke, J. B., Kumar, N., Lima Azevedo, C., & Ben-Akiva, M. (2021)
Transportation, 48(4), 1613-1638.
https://doi.org/10.1007/s11116-020-10106-y - Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore
Oh, S., Seshadri, R., Lima Azevedo, C., Kumar, N., Basak, K., & Ben-Akiva, M. (2020)
Transportation Research Part A: Policy and Practice.
https://doi.org/10.1016/j.tra.2020.06.009 - From Traditional to Automated Mobility on Demand: A Comprehensive Framework for Modeling On-Demand Services in SimMobility
Nahmias-Biran, B.-H., Oke, J. B., Kumar, N., et al. (2019)
Transportation Research Record, 2673(12), 15-29.
https://journals.sagepub.com/doi/10.1177/0361198119853553 - U-PC: Unsupervised Planogram Compliance
Ray, A., Kumar, N., Shaw, A., & Mukherjee, D. P. (2018)
European Conference on Computer Vision (ECCV), 586-600.
ECCV paper link - A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities
Kumar, N., Zhang, Y., Wiedemann, N., Oke, J. B., & Raubal, M. (2025)
Under revision at Scientific Reports; ResearchSquare preprint.
https://www.researchsquare.com/article/rs-4952650/v2
