High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

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.

Publications

  1. 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.
  2. 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.
  3. S. Shekhar et al., URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services, Journal of Systems Architecture, 2020.
  4. M. Wilbur et al., Impact of COVID-19 on Public Transit Accessibility and Ridership, in Preprint at Arxiv, 2020.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.