An HSMM integrated with discrete-time survival analysis is applied to four years of Shanghai metro smart card data to identify five mobility states, directional transitions, and state-dependent exit/re-entry hazards.
In: 2024 7th International Confer- ence on Informatics and Computational Sciences (ICICoS)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
YOLOv11n reports 46.6% mAP@50, 3.2% higher precision, and 22% fewer FLOPs than YOLOv8n on a custom IDD+BDD100K dataset for adverse-weather mixed traffic detection.
citing papers explorer
-
Understanding Long-Term Dynamics of Individual Metro Usage: A Hidden Semi-Markov State Framework with Survival Analysis
An HSMM integrated with discrete-time survival analysis is applied to four years of Shanghai metro smart card data to identify five mobility states, directional transitions, and state-dependent exit/re-entry hazards.
-
Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries
YOLOv11n reports 46.6% mAP@50, 3.2% higher precision, and 22% fewer FLOPs than YOLOv8n on a custom IDD+BDD100K dataset for adverse-weather mixed traffic detection.