5 citations · 11 across the 12 of their papers we have counts for
13 papers
When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift
Haochen Lv, Jianhao Zhang, Zhichen Lei +5
Spatiotemporal forecasting plays an important role in real-world applications, where probabilistic approaches provide uncertainty estimates for decision-making. Real-world systems…
Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference
Xiaowei Mao, Bowen Sui, Weijie Zhang +7
Expressway video anomaly detection is important for traffic safety, but remains challenging across diverse scenes, particularly for far-field vehicles with subtle abnormal motion.…
Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation
Xiaowei Mao, Huihu Ding, Yan Lin +6
Imputing missing values in spatial-temporal traffic data is essential for intelligent transportation systems. Among advanced imputation methods, score-based diffusion models have d…
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
Haochen Lv, Yan Lin, Shengnan Guo +5
Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecastin…
G-KV: Decoding-Time KV Cache Eviction with Global Attention
Mengqi Liao, Lu Wang, Chaoyun Zhang +7
Recent reasoning large language models (LLMs) excel in complex tasks but encounter significant computational and memory challenges due to long sequence lengths. KV cache compressio…
Towards An Efficient and Effective En Route Travel Time Estimation Framework
Zekai Shen, Haitao Yuan, Xiaowei Mao +4
En route travel time estimation (ER-TTE) focuses on predicting the travel time of the remaining route. Existing ER-TTE methods always make re-estimation which significantly hinders…
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services
Youfang Lin, Jinji Fu, Haomin Wen +8
In Location-Based Services (LBS), such as food delivery, a fundamental task is segmenting Areas of Interest (AOIs), aiming at partitioning the urban geographical spaces into non-ov…
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Hanyang Chen, Yang Jiang, Shengnan Guo +3
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC ass…
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Xiaowei Mao, Yan Lin, Shengnan Guo +6
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T).…
STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM
YiHeng Huang, Xiaowei Mao, Shengnan Guo +5
Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but…
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Haomin Wen, Youfang Lin, Lixia Wu +10
Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emergin…
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Xiaowei Mao, Haomin Wen, Hengrui Zhang +5
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service route of a worker given his current task pool, has received rising attention in recent years…
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
Lixia Wu, Haomin Wen, Haoyuan Hu +10
Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed t…