The multi-modal energy-optimal trip scheduling in real-time (METS-R) simulator (SIM) is an agent-based traffic simulator for addressing planning and operational challenges in deploying emerging transportation technologies. In its standalone mode, it can simulate large-scale (e.g., entire city-level) mobility services (i.e., ride-hailing, bus) with electric vehicles. In the high-performance computing (HPC) mode, a cloud-based control can be integrated. This mode facilitates seamless integration with Kafka data streams, allowing for more refined control strategies (e.g., eco-routing, demand-adaptive transit scheduling). Additionally, the HPC mode transforms the METS-R SIM into a parallel environment, making it ideal for extensive data generation tasks. Currently, the METS-R SIM is under active development to enhance the aforementioned features, and to be able to be used together with CARLA as a co-simulator.
METSR_demo.-.Made.with.Clipchamp.mp4
| Resource name | Link |
|---|---|
| The latest document | https://umnilab.github.io/METS-R_doc/ |
| The HPC module | https://github.com/umnilab/METS-R_HPC |
| A visualization demo | https://engineering.purdue.edu/HSEES/METSRVis/ |
| The simulation paper | https://www.sciencedirect.com/science/article/abs/pii/S1569190X24000121 |
The current contributors of METS-R SIM are Zengxiang Lei (lei67@purdue.edu) and Ruichen Tan (tan479@purdue.edu). If you have any questions, please feel free to contact them.
The following people contributed directly to the source code of the METS-R SIM until 2021: Zengxiang Lei, Jiawei Xue, Xiaowei Chen, Charitha Samya, Juan Esteban Suarez Lopez, and Zhenyu Wang.
METS-R SIM was developed based on a hurricane evacuation simulator named A-RESCUE, whose authors are: Xianyuan Zhan, Samiul Hasan, Christopher Thompson, Xinwu Qian, Heman Gelhot, Wenbo Zhang, Zengxiang Lei, and Rajat Verma.
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Lei, Z., Xue, J., Chen, X., Qian, X., Saumya, C., He, M., ... & Ukkusuri, S. V. (2024). METS-R SIM: A simulator for Multi-modal Energy-optimal Trip Scheduling in Real-time with shared autonomous electric vehicles. Simulation Modelling Practice and Theory, 132, 102898.
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Lei, Z., Xue, J., Chen, X., Saumya, C., Qian, X., He, M., ... & Ukkusuri, S. V. (2021). ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 1658-1663). IEEE
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Chen, X., Xue, J., Lei, Z., Qian, X., & Ukkusuri, S. V. (2022). Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance. Transportation Research Part D: Transport and Environment, 111, 103447.
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Qian, X., Xue, J., & Ukkusuri, S. V. (2021). Demand-adaptive route planning and scheduling for urban hub-based high-capacity mobility-on-demand services. Accepted in ISTTT 24 Proceedings.
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Qian, X., Xue, J., Sobolevsky, S., Yang, C., & Ukkusuri, S. (2019). Stationary spatial charging demand distribution for commercial electric vehicles in urban area. In 2019 IEEE intelligent transportation systems conference (ITSC) (pp. 220-225). IEEE.
- Chen, X., Lei, Z., & Ukkusuri, S. V. (2024). Modeling the influence of charging cost on electric ride-hailing vehicles. Transportation Research Part C: Emerging Technologies, 160, 104514.