50 citations · 206 across the 34 of their papers we have counts for
37 papers
Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
Chonghe Jiang, Ao Qu, Siyuan Liu +9
Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining execut…
EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards
Zhiqing Cui, Xinxiang Yin, Yihong Tang +11
Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards make this work consequential be…
Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
Donghang Li, Dingyi Zhuang, Yunlin Li +5
New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds t…
Ozone: A Unified Platform for Transportation Research
Ou Zheng, Ruyi Feng, Yufeng Yang +12
Transportation research increasingly depends on heterogeneous data from roadside cameras, UAV imagery, LiDAR, and in-vehicle sensors. Differences in data representations, model int…
Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
Yihong Tang, Tong Nie, Junlin He +3
Forecasting urban delivery demand becomes substantially more challenging when newly added service regions lack historical records. Existing spatiotemporal forecasters effectively m…
Risk-Controllable Multi-View Diffusion for Driving Scenario Generation
Hongyi Lin, Wenxiu Shi, Heye Huang +5
Generating safety-critical driving scenarios is crucial for evaluating and improving autonomous driving systems, but long-tail risky situations are rarely observed in real-world da…
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Dahai Yu, Rongchao Xu, Dingyi Zhuang +3
Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep lea…
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang, Kangxin Cui, Jung Ho Park +6
Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matc…
Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
Haicheng Liao, Huanming Shen, Bonan Wang +8
Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typi…
TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Lingyu Jiang, Lingyu Xu, Peiran Li +14
We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL)…
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Minwei Kong, Ao Qu, Xiaotong Guo +12
Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…
Alternative Graph Neural Networks: Synergizing GEV Models and Deep Learning for Travel Mode Choice Modeling
Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang +3
Generalized extreme value models capture dependence among choice alternatives in discrete choice modeling, but require this dependence to be predefined, symmetric, and shared unifo…
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Dahai Yu, Dingyi Zhuang, Lin Jiang +5
Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In…
Interpretable Time Series Autoregression for Periodicity Quantification
Xinyu Chen, Vassilis Digalakis, Lijun Ding +2
Time series autoregression (AR) is a classical tool for modeling auto-correlations and periodic structures in real-world systems. We revisit this model from an interpretable machin…
Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models
Mingyi He, Yuebing Liang, Shenhao Wang +5
Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals and stakeholders. The advent of…
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Yutong Xia, Ao Qu, Yunhan Zheng +8
Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are ofte…
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Dingyi Zhuang, Hanyong Xu, Xiaotong Guo +3
Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Gr…
Virtual Nodes Improve Long-term Traffic Prediction
Xiaoyang Cao, Dingyi Zhuang, Jinhua Zhao +1
Effective traffic prediction is a cornerstone of intelligent transportation systems, enabling precise forecasts of traffic flow, speed, and congestion. While traditional spatio-tem…
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning
Yihong Tang, Ao Qu, Zhaokai Wang +7
Vision language models (VLMs) perform well on many tasks but often fail at spatial reasoning, which is essential for navigation and interaction with physical environments. Many spa…
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng +2
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly p…
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Dingyi Zhuang, Yuheng Bu, Guang Wang +2
Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the i…
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
Dingyi Zhuang, Qingyi Wang, Yunhan Zheng +4
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typi…
Time Series Supplier Allocation via Deep Black-Litterman Model
Jiayuan Luo, Wentao Zhang, Yuchen Fang +4
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficie…
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
Yihong Tang, Zhaokai Wang, Ao Qu +11
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this p…