Related Experiment Video
Updated: Nov 7, 2025
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
13.8K
A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN
Zihe Huang1, Shangbing Gao2, Chuangxin Cai1
1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, People's Republic of China.
Scientific Reports
|May 4, 2021
Summary
A new DBSCAN+ algorithm improves taxi data clustering speed and accuracy for identifying urban passenger hotspots. This visual analysis method aids city planning and traffic efficiency.
Area of Science:
- Urban informatics
- Data science
- Geospatial analysis
Background:
- City growth and vehicle connectivity necessitate advanced visual analysis for urban perception.
- Traditional density-based clustering algorithms struggle with large-scale taxi trajectory data, exhibiting slow processing and inability to identify cluster centers effectively.
Purpose of the Study:
- To address the limitations of traditional algorithms in handling large-scale taxi trajectory data.
- To propose an improved clustering algorithm for efficient identification of urban passenger hotspots.
Main Methods:
- Data preprocessing involved cleaning passenger points from raw trajectory data.
- A novel DBSCAN+ (density-based spatial clustering of applications with noise plus) algorithm was developed and applied.
- Massive passenger points were cyclically sliced and clustered, extracting centers based on maximum density.
Main Results:
- The DBSCAN+ algorithm demonstrated significant advantages in clustering speed, precision, and visualization compared to existing methods.
- Effective identification of large-scale city passenger hotspots was achieved.
- The method successfully visualized clustering results for urban analysis.
Conclusions:
- The proposed DBSCAN+ algorithm offers a superior solution for clustering large-scale urban passenger trajectory data.
- This approach provides valuable insights for urban planning and enhances traffic efficiency.
