Publications

2024

1. NeurIPS24

   

   RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks

   Jiaxing Zhang, Zhuomin Chen, Hao Mei , and 3 more authors

   In Proceedings of the 2024 Neural Information Processing Systems , 2024

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   Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks. However, the inference process is often not interpretable. Most existing explanation techniques are limited to understanding GNN behaviors in classification tasks. In this work, we seek an explanation to interpret the graph regression models (XAIG-R). We show that existing methods overlook the distribution shifting and continuously ordered decision boundary, which hinders them away from being applied in the regression tasks. To address these challenges, we propose a novel objective based on the information bottleneck theory and introduce a new mix-up framework, which could support various GNNs in a model-agnostic manner. We further present a contrastive learning strategy to tackle the continuously ordered labels in regression task. To empirically verify the effectiveness of the proposed method, we introduce three benchmark datasets and a real-life dataset for evaluation. Extensive experiments show the effectiveness of the proposed method in interpreting GNN models in regression tasks.

   @inproceedings{da2024prompt,
     title = {RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks},
     author = {Zhang, Jiaxing and Chen, Zhuomin and Mei, Hao and Da, Longchao and Luo, Dongsheng and Wei, Hua},
     booktitle = {Proceedings of the 2024 Neural Information Processing Systems},
     year = {2024},
   }

2. AAAI24

   

   Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

   Longchao Da, Minquan Gao, Hao Mei , and 1 more author

   In Proceedings of the AAAI Conference on Artificial Intelligence , 2024

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   Numerous solutions are proposed for the Traffic Signal Con- trol (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities’ congestion problems. However, performance gaps still exist when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the sys- tem dynamic difference between the training simulators and the real-world environments. In this work, we leverage the knowledge of Large Language Models (LLMs) to understand and profile the system dynamics by a prompt-based grounded action transformation to bridge the performance gap. Specifi- cally, this paper exploits the pre-trained LLM’s inference abil- ity to understand how traffic dynamics change with weather conditions, traffic states, and road types. Being aware of the changes, the policies’ action is taken and grounded based on realistic dynamics, thus helping the agent learn a more realistic policy. We conduct experiments on four different scenarios to show the effectiveness of the proposed PromptGAT’s ability to mitigate the performance gap of reinforcement learning from simulation to reality (sim-to-real).

   @inproceedings{da2024prompu,
     title = {Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning},
     author = {Da, Longchao and Gao, Minquan and Mei, Hao and Wei, Hua},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     year = {2024},
     doi = {10.1609/aaai.v38i1.27758},
   }

3. AAAI24

   

   Probabilistic Offline Policy Ranking with Approximate Bayesian Computation

   Longchao Da, Porter Jenkins, Trevor Schwantes , and 2 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence , 2024

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   In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy evaluation (OPE). However, they fail to analyze special case performance (e.g., worst or best cases), due to the lack of holistic characterization of policies’ performance. It is even more difficult to estimate precise policy values when the reward is not fully accessible under sparse settings. In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking. POPR does not rely on value estimation, and the derived performance posterior can be used to distinguish candidates in worst-, best-, and average-cases. To estimate the posterior, we propose POPR-EABC, an Energy-based Approximate Bayesian Computation (ABC) method conducting likelihood-free inference. POPR-EABC reduces the heuristic nature of ABC by a smooth energy function, and improves the sampling efficiency by a pseudo-likelihood. We empirically demonstrate that POPR-EABC is adequate for evaluating policies in both discrete and continuous action spaces across various experiment environments, and facilitates probabilistic comparisons of candidate policies before deployment.

   @inproceedings{da2024probabilistic,
     title = {Probabilistic Offline Policy Ranking with Approximate Bayesian Computation},
     author = {Da, Longchao and Jenkins, Porter and Schwantes, Trevor and Dotson, Jeffrey and Wei, Hua},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     volume = {38},
     number = {18},
     pages = {20370--20378},
     year = {2024},
     doi = {10.1609/aaai.v38i18.30019},
   }

4. IJMLC

   

   Open-ti: Open traffic intelligence with augmented language model

   Longchao Da, Kuanru Liou, Tiejin Chen , and 4 more authors

   International Journal of Machine Learning and Cybernetics, 2024

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   Transportation has greatly benefited the cities’ development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people’s daily commuting efficiency. However, intelligent transportation, as a cross-discipline, often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained large language models, especially the potential of being augmented with abilities to understand and execute intricate commands, we introduce Open-TI. Serving as a bridge to mitigate the industry-academic gap, Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence, it is augmented with the capability to harness external traffic analysis packages based on existing conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch—spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We eventually provide the formal implementation structure, and the open-ended design invites further community-driven enhancements. A demo video is provided at: https://youtu.be/pZ4-5PXz9Xs.

   @article{da2024open,
     title = {Open-ti: Open traffic intelligence with augmented language model},
     author = {Da, Longchao and Liou, Kuanru and Chen, Tiejin and Zhou, Xuesong and Luo, Xiangyong and Yang, Yezhou and Wei, Hua},
     journal = {International Journal of Machine Learning and Cybernetics},
     pages = {1--26},
     year = {2024},
     publisher = {Springer},
     doi = {10.1007/s13042-024-02190-8},
   }

5. ECML-PKDD24

   

   CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models

   Longchao Da, Chen Chu, Weinan Zhang , and 1 more author

   arXiv preprint arXiv:2402.06127, 2024

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   Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional traffic simulators, such as SUMO and CityFlow, are often limited by their reliance on rule-based models with hyperparameters that oversimplify driving behaviors, resulting in unrealistic simulations. To enhance realism, some simulators have provided Application Programming Interfaces (APIs) to interact with Machine Learning (ML) models, which learn from observed data and offer more sophisticated driving behavior models. However, this approach faces challenges in scalability and time efficiency as vehicle numbers increase. Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation. CityFlowER innovatively pre-embeds ML models within the simulator, eliminating the need for external API interactions and enabling faster data computation. This approach allows for a blend of rule-based and ML behavior models for individual vehicles, offering unparalleled flexibility and efficiency, particularly in large-scale simulations. We provide detailed comparisons with existing simulators, implementation insights, and comprehensive experiments to demonstrate CityFlowER’s superiority in terms of realism, efficiency, and adaptability.

   @article{da2024cityflower,
     title = {CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models},
     author = {Da, Longchao and Chu, Chen and Zhang, Weinan and Wei, Hua},
     journal = {arXiv preprint arXiv:2402.06127},
     year = {2024},
   }

6. CIKM24

   

   Shaded Route Planning Using Active Segmentation and Identification of Satellite Images

   Longchao Da, Rohan Chhibba, Rushabh Jaiswal , and 2 more authors

   arXiv preprint arXiv:2407.13689, 2024

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   Heatwaves pose significant health risks, particularly due to prolonged exposure to high summer temperatures. Vulnerable groups, especially pedestrians and cyclists on sun-exposed sidewalks, motivate the development of a route planning method that incorporates somatosensory temperature effects through shade ratio consideration. This paper is the first to introduce a pipeline that utilizes segmentation foundation models to extract shaded areas from high-resolution satellite images. These areas are then integrated into a multi-layered road map, enabling users to customize routes based on a balance between distance and shade exposure, thereby enhancing comfort and health during outdoor activities. Specifically, we construct a graph-based representation of the road map, where links indicate connectivity and are updated with shade ratio data for dynamic route planning. This system is already implemented online, with a video demonstration, and will be specifically adapted to assist travelers during the 2024 Olympic Games in Paris.

   @article{da2024shaded,
     title = {Shaded Route Planning Using Active Segmentation and Identification of Satellite Images},
     author = {Da, Longchao and Chhibba, Rohan and Jaiswal, Rushabh and Middel, Ariane and Wei, Hua},
     journal = {arXiv preprint arXiv:2407.13689},
     year = {2024},
   }

7. ITSC24

   

   SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras

   Tiejin Chen, Prithvi Shirke, Bharatesh Chakravarthi , and 5 more authors

   2024

   Abs Bib Code

   This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control which aim to provide simplified feature vectors like vehicle counts from traffic simulators, SynTraC provides real-style images from the CARLA simulator with annotated features, along with traffic signal states. This image-based dataset comes with diverse real-world scenarios, including varying weather and times of day. Additionally, SynTraC also provides different reward values for advanced traffic signal control algorithms like reinforcement learning. Experiments with SynTraC demonstrate that it is still an open challenge to image-based traffic signal control methods compared with feature-based control methods, indicating our dataset can further guide the development of future algorithms.

   @misc{chen2024syntracsyntheticdatasettraffic,
     title = {SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras},
     author = {Chen, Tiejin and Shirke, Prithvi and Chakravarthi, Bharatesh and Vaghela, Arpitsinh and Da, Longchao and Lu, Duo and Yang, Yezhou and Wei, Hua},
     year = {2024},
     eprint = {2408.09588},
     archiveprefix = {arXiv},
     url = {https://arxiv.org/abs/2408.09588}
   }

2023

1. CASE23

   

   Sim2Real Transfer for Traffic Signal Control

   Longchao Da, Hao Mei, Romir Sharma , and 1 more author

   In IEEE 19th International Conference on Automation Science and Engineering , 2023

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   Traffic signal control is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but transferring learned policies from simulation to the real world remains a challenge due to the domain gap between the simulation and the complex real-life scenario. In this paper, we utilize grounded action transformation to mitigate the domain shifting problem and improve Sim2Real transfer for RL-based traffic signal control. Grounded action transformation leverages the dynamics between the simulation and real-world actions to generate effective real-world actions. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world. Our results demonstrate the potential of grounded action transformation as a promising technique for Sim2Real transfer in RL-based traffic signal control.

   @inproceedings{da2023sim2real,
     title = {Sim2Real Transfer for Traffic Signal Control},
     author = {Da, Longchao and Mei, Hao and Sharma, Romir and Wei, Hua},
     booktitle = {IEEE 19th International Conference on Automation Science and Engineering},
     pages = {1--2},
     year = {2023},
     organization = {IEEE},
   }

2. Machine Learning

   

   Libsignal: an open library for traffic signal control

   Hao Mei, Xiaoliang Lei, Longchao Da , and 2 more authors

   Machine Learning, 2023

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   This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.

   @article{mei2023libsignal,
     title = {Libsignal: an open library for traffic signal control},
     author = {Mei, Hao and Lei, Xiaoliang and Da, Longchao and Shi, Bin and Wei, Hua},
     journal = {Machine Learning},
     pages = {1--37},
     year = {2023},
     publisher = {Springer},
   }

3. CDC23

   

   Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

   Longchao Da, Hao Mei, Romir Sharma , and 1 more author

   In 2023 62nd IEEE Conference on Decision and Control (CDC) , 2023

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   Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained in simulation and suffer from the performance gap between simulation and the real world. In this paper, we propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT, which transfers a learned policy trained from a simulated environment to a real-world environment by dynamically transforming actions in the simulation with uncertainty to mitigate the domain gap of transition dynamics. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world.

   @inproceedings{da2023uncertainty,
     title = {Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control},
     author = {Da, Longchao and Mei, Hao and Sharma, Romir and Wei, Hua},
     booktitle = {2023 62nd IEEE Conference on Decision and Control (CDC)},
     pages = {1124--1129},
     year = {2023},
     organization = {IEEE},
   }

2022

1. ERA

   

   CrowdGAIL: A spatiotemporal aware method for agent navigation

   Longchao Da, and Hua Wei

   Electronic Research Archive, 2022

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   Agent navigation has been a crucial task in today’s service and automated factories. Many efforts are to set specific rules for agents in a certain scenario to regulate the agent’s behaviors. However, not all situations could be in advance considered, which might lead to terrible performance in a real-world application. In this paper, we propose CrowdGAIL, a method to learn from expert behaviors as an instructing policy, can train most ’human-like’ agents in navigation problems without manually setting any reward function or beforehand regulations. First, the proposed model structure is based on generative adversarial imitation learning (GAIL), which imitates how humans take actions and move toward the target to a maximum extent, and by comparison, we prove the advantage of proximal policy optimization (PPO) to trust region policy optimization, thus, GAIL-PPO is what we base. Second, we design a special Sequential DemoBuffer compatible with the inner long short-term memory structure to apply spatiotemporal instruction on the agent’s next step. Third, the paper demonstrates the potential of the model with an integrated social manner in a multi-agent scenario by considering human collision avoidance as well as social comfort distance. At last, experiments on the generated dataset from CrowdNav verify how close our model would act like a human being in the trajectory aspect and also how it could guide the multi-agents by avoiding any collision. Under the same evaluation metrics, CrowdGAIL shows better results compared with classic Social-GAN.

   @article{da2022crowdgail,
     title = {CrowdGAIL: A spatiotemporal aware method for agent navigation},
     author = {Da, Longchao and Wei, Hua},
     journal = {Electronic Research Archive},
     volume = {31},
     number = {2},
     year = {2022},
   }