paper | abstract | bibtex

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

@inproceedings{li2023eco,
  title={Eco-pinn: A physics-informed neural network for eco-toll estimation},
  author={Li, Yan and Yang, Mingzhou and Eagon, Matthew and Farhadloo, Majid and Xie, Yiqun and Northrop, William F and Shekhar, Shashi},
  booktitle={Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)},
  pages={838--846},
  year={2023},
  organization={SIAM}
}

paper | abstract | bibtex

Society must achieve net zero carbon emissions to mitigate anthropogenic climate change and preserve a livable planet. Reducing transportation emissions is an important component to achieve net zero because such emissions account for a quarter of global carbon released into the environment. Driven by increasingly available transportation big data and enhanced computational speed, data mining techniques have become powerful tools to achieve transportation decarbonization. This paper describes existing gaps in transportation decarbonization research where data mining can help address problems related to medium and heavy vehicle electrification, electric micromobility safety, and analysis of alternative fuel-powered and plug-in hybrid electric vehicles. Our recommendations encompass open research problems, opportunities for data mining applications, and examples of areas where advancements in data mining techniques are needed. We encourage the data mining community to explore these challenges and opportunities to help achieve net zero emissions goals.

@inproceedings{yang2023data,
  title={Data Mining Challenges and Opportunities to Achieve Net Zero Carbon Emissions: Focus on Electrified Vehicles},
  author={Yang, Mingzhou and Jayaprakash, Bharat and Eagon, Matthew and Jung, Hyeonjung and Northrop, William F and Shekhar, Shashi},
  booktitle={Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)},
  pages={953--956},
  year={2023},
  organization={SIAM}
}

paper | abstract | bibtex

Electric vehicles (EVs) have been identified as one of the necessary solutions to reduce the carbon footprint of transportation, a large source of greenhouse gas (GHG). As adoption of EVs and infrastructures to support them grow, formidable hurdles to achieving equitable economic growth and reliable transportation and energy system via effective management of EVs have been discovered. This opens major opportunities and challenges for spatial computing research. Equitable distribution of EV infrastructure in a broad region presents complicated spatial computing challenges with a great social impact. Spatial computing informed adoption and management of EVs will be essential to achieving the maximum carbon reduction through EVs along with a reliable transition to a renewable energy future. On the road, EV drivers may benefit from spatial computing to choose routes that take into account public fast-charging stations as well as energy needs of the route, such as speed, weather (e.g. air-conditioning, heating, and elevation changes). This paper presents open research questions of spatial computing related to EV management.

@inproceedings{jung2022revolutionizing,
  title={Revolutionizing electric vehicle management: Spatial computing challenges and opportunities},
  author={Jung, Hyeonjung and Yang, Mingzhou and Eagon, Matthew and Northrop, William},
  booktitle={Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science},
  pages={1--4},
  year={2022}
}
    

paper | abstract | bibtex

Public transports, such as subway lines and buses, offer affordable ride-sharing services and reduce the road network traffic. Extracting passengers’ preferences from their public transit choices is important to city planners but technically non-trivial. When traveling by taking public transits, passengers make sequences of transit choices, and their rewards are usually influenced by other passengers’ choices. This process can be modeled as a Markov Game (MG). In this paper, we make the first effort to model travelers’ preferences of making transit choices using MGs. Based on the discovery that passengers usually do not change their policies, we propose novel algorithms to extract reward functions from the observed deterministic equilibrium joint policy of all agents in a general-sum MG to infer travelers’ preferences. First, we assume we have the access to the entire joint policy. We characterize the set of all reward functions for which the given joint policy is a Nash equilibrium policy. In order to remove the degeneracy of the solution, we then attempt to pick reward functions so as to maximize the sum of the deviation between the the observed policy and the sub-optimal policy of each agent. This results in a skillfully solvable linear programming algorithm for the multi-agent inverse reinforcement learning (MA-IRL) problem. Then, we deal with the case where we have access to the equilibrium joint policy through a set of actual trajectories. We propose an iterative algorithm inspired by single-agent apprenticeship learning algorithms and the cyclic coordinate descent approach. We evaluate the proposed algorithms on both a simple Grid Game and a unique real-world dataset (from Shenzhen, China). Results show that when we have access to the full policy, our algorithm can efficiently recover most of the reward structure, especially the interaction of agents. In the case where we only have access to a set of sampled expert trajectories, our algorithm can provide an explanation of the expert trajectories. Measured with respect to the experts’ unknown reward function, the performance of the policy output by our algorithm is close to that of the expert policy.

@inproceedings{yang2020inferring,
  title={Inferring passengers interactive choices on public transits via ma-al: Multi-agent apprenticeship learning},
  author={Yang, Mingzhou and Li, Yanhua and Zhou, Xun and Lu, Hui and Tian, Zhihong and Luo, Jun},
  booktitle={Proceedings of The Web Conference 2020},
  pages={1637--1647},
  year={2020}
}