Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Sources
2.2.3. Adaptive Dissimilarity Index
2.2.4. Construction of the Similarity Measurement Function
2.3. Collective Human Mobility Spatial-Temporal Pattern Recognition
2.3.1. Clustering Method for the Travel Time Series
2.3.2. Comparing the Results with the K-Means Method
3. Results
3.1. Screening the Aggregate Units
3.2. Extraction of the Spatiotemporal Travel Patterns
3.2.1. Classification of the Travel Time Series
3.2.2. Spatial Distribution of the Travel Patterns
4. Discussion
5. Conclusions
- We used the DBSCAN algorithm, which can effectively eliminate noise, to cluster the taxi travel time series data, and seven departure patterns and six arrival patterns were obtained. Finally, seven human mobility patterns were delimited through spatial matching based on the aggregate units.
- Using the random forest algorithm, this paper established a correlation model between the mobility patterns and POI features. Using the feature importance and feature contribution measures as indicators, it was verified that the different urban regional functions had different driving mechanisms for the various taxi travel patterns.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature ID | Feature Description | Urban Construction Land Category |
---|---|---|
X1 | Residential area | Residential |
X2 | Government agency | Administrative office |
X3 | Cultural facilities | Cultural facility |
X4 | School/research institute | Educational /Research |
X5 | Hospital | Medical |
X6 | Scenic area | Cultural relics and historic sites; religious facilities |
X7 | Restaurant | Commercial facility |
X8 | Hostel | Commercial facility |
X9 | Entertainment venue | Commercial facility |
X10 | Supermarket | Commercial facility |
X11 | Department store | Commercial facility |
X12 | Retail store | Commercial facility |
X13 | Commercial Building | Commercial facility |
X14 | Bank | Commercial facility |
X15 | Company | Commercial facility; Industrial |
X16 | Cinema | Recreation and wellness facilities |
X17 | Wellness facility | Recreation and wellness facilities |
X18 | Subway station | Urban rail transit |
X19 | Transportation hub | Transportation hub |
X20 | Bus station | Traffic station site |
X21 | Parking lot | Traffic station site |
X22 | Park/garden | Green space |
Simulation Mode | Mode 1 | Mode 2 | Mode 3 | Mode 4 | Mode 5 | Mode 6 | Mode 7 | |
---|---|---|---|---|---|---|---|---|
Actual Mode | ||||||||
Mode 1 | 7 | 0 | 0 | 1 | 1 | 0 | 0 | |
Mode 2 | 0 | 9 | 0 | 0 | 6 | 0 | 0 | |
Mode 3 | 0 | 0 | 7 | 0 | 5 | 0 | 0 | |
Mode 4 | 0 | 0 | 0 | 22 | 14 | 1 | 1 | |
Mode 5 | 0 | 0 | 0 | 15 | 92 | 4 | 0 | |
Mode 6 | 0 | 0 | 0 | 1 | 2 | 15 | 0 | |
Mode 7 | 0 | 0 | 0 | 3 | 3 | 1 | 2 |
Mode | Precision | Recall | F1-Score | Support |
---|---|---|---|---|
Mode 1 | 1.00 | 0.78 | 0.88 | 9 |
Mode 2 | 1.00 | 0.60 | 0.75 | 15 |
Mode 3 | 1.00 | 0.58 | 0.74 | 12 |
Mode 4 | 0.52 | 0.58 | 0.55 | 38 |
Mode 5 | 0.75 | 0.83 | 0.79 | 111 |
Mode 6 | 0.71 | 0.83 | 0.77 | 18 |
Mode 7 | 0.67 | 0.22 | 0.33 | 9 |
Weighted average | 0.74 | 0.73 | 0.72 | 212 |
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Yang, J.; Sun, Y.; Shang, B.; Wang, L.; Zhu, J. Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data. Sensors 2019, 19, 2812. https://doi.org/10.3390/s19122812
Yang J, Sun Y, Shang B, Wang L, Zhu J. Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data. Sensors. 2019; 19(12):2812. https://doi.org/10.3390/s19122812
Chicago/Turabian StyleYang, **g, Yizhong Sun, Bowen Shang, Lei Wang, and Jie Zhu. 2019. "Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data" Sensors 19, no. 12: 2812. https://doi.org/10.3390/s19122812