This is a project funded by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0008467. The research approach is to use continuous monitoring sensors on the complete mix of CARTA transit buses and to develop predictors and optimization mechanisms using the data. Specific activities are:
- Acquire high-resolution (updated every minute) Spatio-temporal telemetry data from CARTA vehicles and exogenous data sources, such as traffic and weather
- Develop an efficient framework to store and process the operational data and external data, including street and elevation maps
- Create macro-level energy predictor using route information and general fleet parameters
- Create a higher-resolution micro model that is tuned to specific vehicle parameters
- Create an optimization framework to select the optimal assignment of vehicles to trips with the goal of reducing overall energy consumption
- Develop a visualization framework to analyze the data
The following slide deck provides a brief overview of the project.
For details please see smarttransit.ai.
- A. Ayman, M. Wilbur, A. Sivagnanam, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets, in 2020 IEEE International Conference on Smart Computing (SMARTCOMP) (SMARTCOMP 2020), Bologna, Italy, 2020.
- J. P. Talusan, M. Wilbur, A. Dubey, and K. Yasumoto, On Decentralized Route Planning Using the Road Side Units as Computing Resources, in 2020 IEEE International Conference on Fog Computing (ICFC), 2020.
- S. Shekhar et al., URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services, Journal of Systems Architecture, 2020.
- M. Wilbur et al., Impact of COVID-19 on Public Transit Accessibility and Ridership, in Preprint at Arxiv, 2020.
- A. Sivagnanam, A. Ayman, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service, in Preprint at Arxiv, 2020.
- Y. Chen, G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese, A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles, in Preprint at Arxiv, 2020.
- S. Basak, A. Dubey, and B. P. Leao, Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks, in IEEE Big Data, Los Angeles, Ca, 2019.
- S. Basak, F. Sun, S. Sengupta, and A. Dubey, Data-Driven Optimization of Public Transit Schedule, in Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, 2019, pp. 265–284.
- S. Basak, A. Aman, A. Laszka, A. Dubey, and B. Leao, Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks, in Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM), 2019.
- A. Oruganti, S. Basak, F. Sun, H. Baroud, and A. Dubey, Modeling and Predicting the Cascading Effects of Delay in Transit Systems, in Transportation Research Board Annual Meeting, 2019.