Mobiliti Core

1. Chan, C., Kuncheria, A., & Macfarlane, J. (2023). Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems. ACM Transactions on Modeling and Computer Simulation, 33(1–2), 7:1-7:29. https://doi.org/10.1145/3579842
2. Chan, C., Wang, B., Bachan, J., & Macfarlane, J. (2018). Mobiliti: Scalable Transportation Simulation Using High-Performance Parallel Computing. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 634–641. https://doi.org/10.1109/ITSC.2018.8569397 
3. Chan, C., Kuncheria, A., Zhao, B., Cabannes, T., Keimer, A., Wang, B., Bayen, A., & Macfarlane, J. (2021). Quasi-Dynamic Traffic Assignment using High Performance Computing. ArXiv:2104.12911 [Cs]. http://arxiv.org/abs/2104.12911

1. Kuncheria, A., Walker, J.L. and Macfarlane, J. (2023). Socially-aware evaluation framework for transportation, Transportation Letters, pp. 1–18. Available at: https://doi.org/10.1080/19427867.2022.2157366.
2. Faltesek, A., Dakshinamoorthi, B., Prabhala, S., Thobani, A., Kuncheria, A., & Macfarlane, J. (2021). Urban Traffic Simulation: Network and Demand Representation Impacts on Congestion Metrics.
3. Deodhar, K., Laurence, C., & Macfarlane, J. (2019). Designing for Mode Shift Opportunity with Metropolitan Scale Simulation. Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, 1–6. https://doi.org/10.1145/3357492.3358634
4. Bulusu, V., Onat, E. B., Sengupta, R., Yedavalli, P., & Macfarlane, J. (2021). A Traffic Demand Analysis Method for Urban Air Mobility. IEEE Transactions on Intelligent Transportation Systems, 22(9), 6039–6047. https://doi.org/10.1109/TITS.2021.3052229 
5. Wang, B., Chan, C., Somasi, D., Macfarlane, J., & Rask, E. (2019). Data-Driven Energy Use Estimation in Large Scale Transportation Networks. Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, 1–6. https://doi.org/10.1145/3357492.3358632

1. Vlachogiannis, D. M., Wei, H., Moura, S., & Macfarlane, J. (2023). HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control. arXiv preprint arXiv:2304.03697.
2. Vlachogiannis, D. M., Moura, S., & Macfarlane, J. (2023). Intersense: An XGBoost model for traffic regulator identification at intersections through crowdsourced GPS data. Transportation Research Part C: Emerging Technologies, 151, 104112. https://doi.org/10.1016/j.trc.2023.104112

1. Mallick, T., Balaprakash, P., Rask, E., & Macfarlane, J. (2021). Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. 2020 25th International Conference on Pattern Recognition (ICPR), 10367–10374. https://doi.org/10.1109/ICPR48806.2021.941327
2. Mallick, T., Balaprakash, P., Rask, E., & Macfarlane, J. (2020). Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting. Transportation Research Record, 2674(9), 473–488. https://doi.org/10.1177/0361198120930010

1. Macfarlane, J., & Xu, B. (2017). Temporal Sampling Constraints for GeoSpatial Path Reconstruction in a Transportation Network. Proceedings of the 10th ACM SIGSPATIAL Workshop on Computational Transportation Science, 1–6. https://doi.org/10.1145/3151547.3151548
2. Jane Macfarlane, Ph.D. “Mobile Device Data Analytics for Next-Generation Traffic Management (Report),” 2021. https://doi.org/10.7922/G2SX6BGF.
3. Macfarlane, Jane. “The Transforming Transportation Ecosystem—A Call to Action,” 2019.
4. Macfarlane, Jane. “When Apps Rule the Road: The Proliferation of Navigation Apps Is Causing Traffic Chaos. It’s Time to Restore Order.” IEEE Spectrum 56, no. 10 (2019): 22–27.