Artificial Intelligence

Missing traffic data caused by sensor failures or communication errors significantly hinders the efficiency of downstream tasks in Intelligent Transportation Systems (ITS), such as the critical functions of traffic monitoring and decision-making. Considering the complex distribution of missing data, it is essential to incorporate the missing features to extract dynamic spatial-temporal correlations in traffic processes. Motivated by these concerns, a novel Dynamic Spatial-Temporal Imputation Network with Missing Features (DSTMIN) is proposed to accurately impute traffic data.

This dataset was synthesized through a large model. We deployed two large models, one playing the role of an elderly patient and the other playing the role of a nurse. The scenario we define is as follows: the patient lies on a deformed bed, conveying clear or unclear needs to the nurse. The nurse analyzes the patient's true needs based on their language, provides a humanized and personalized response to the patient, and issues control commands to the deformed bed. The dataset consists of two parts: patient statements - answers and patient statements - control commands.

we introduce our Hard Defect Classification Dataset (HDCD), which is primarily constructed based on the Multi Classifier Data (MCD) [38], a multi-source dataset comprising self-collected image samples along with contributions from several Departments of transportation inspection databases. Initially, we integrated data from diverse sources based on the defect classification criteria of the MCD. Subsequently, we partitioned the dataset into a training set and a test set. To simulate real-world challenges, we specifically curated the test set to include hard samples

This dataset analyzes rail transit carriage occupancy levels, categorizing crowd density into three distinct classifications. The data collection process involved systematic monitoring of passenger distribution within subway cars during various operational hours, encompassing peak and off-peak periods. Each classification represents different degrees of crowding, providing valuable insights into passenger flow patterns and capacity utilization.