Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example
Abstract
:1. Introduction
2. Data
2.1. Land Use Data for Haikou
2.2. LJ1-01 Nighttime Light Data
2.3. Taxi Data
- As a typical representative of a tourism city, Haikou has a large proportion of the mobile population (or called “tourists”) and a low rate of private car ownership compared with conventional cities, especially in the peak tourism season selected for this paper. For the group of tourists studied in this research, car-hailing services become a valuable tool at night, facilitating their travel while extending the time of economic activity;
- Didi accounted for over 90% of all car-hailing services in China at the time of the statistics. Simultaneously, all cruising taxis in Haikou have been connected to and integrated with the Didi platform since 2017. Therefore, the taxi order data counted by Didi include dominated car-hailing taxis and traditional taxi data;
- According to information found from Haikou Government, most bus and shuttle services operate from 6 a.m. to 10 p.m. Additionally, due to known safety hazards, the number of people walking late at night is very rare and difficult to count, and the use of shared transportation at night is significantly reduced [36]. In summary, from 9 p.m. to 5 am, the evening peak hour has ended, and public transportation services have also almost been suspended; thus, taxi orders are the most accurate reflection of human night mobility characteristics.
3. Methods
3.1. Geogrid-Based Analysis
3.2. Spatial-Temporal Dimensionality Reduction and Clustering of Crowd Flows
3.2.1. DCNMF Dimensionality Reduction
Algorithm 1. Dual Semi-Supervised Convex NMF (DCNMF) algorithm. |
Input: Data matrix , label constraint matrix , parameter K = 4. |
Output: a base matrix and a coefficient matrix . |
1: Initialize matrices W and Z; |
2: Construct the weight matrix v by using CPA; |
3: Update by using (9); |
4: Update by using (10); |
5: If not Convergence, return step (3); |
6: , . |
3.2.2. Clustering of Crowd Flows’ Changes Based on Geogrids
Algorithm 2. Dual Semi-Supervised Convex NMF (DCNMF) algorithm. |
Input: coefficient matrix , label constraint matrix , cluster types = 4. |
Output: geogrid cluster type matrix (“pick-up”, “drop-off” and “in-out” table). |
1: Select samples of different types in as the initial clustering center,; |
2: For each sample ith column in , calculate the distance from sample to the cluster centers; |
3: divide each sample into the class corresponding to the cluster center with the smallest distance; |
4: For each cluster type, recalculate its cluster center ; |
5: If not Convergence, return step (2); |
6: If not complete three tables, return step (1); |
7: Output geogrid nighttime crowd flows’ changes cluster type matrix in three flow tables. |
3.2.3. Methodological Evaluation
4. Results
4.1. Statistical Analysis of Taxi Flows with Land Use Geogrid
4.2. Changes in Nighttime Patterns of Crowd Flows Aimed at Different Time Periods
4.3. A Comparison of Citywide and Downtown Crowd Flows
4.4. Influence of the Nighttime Light Intensity on the Pattern of Crowd Flows
5. Discussion
6. Conclusions
- By incorporating land use data, the crowd flows obtained by taxi services are no longer limited to point-to-point analysis, which provides a baseline for future grid-based analysis in smart cities;
- Due to the high resolution of LJ1-01 in east Asia, the association with the LJ1-01 nighttime light image makes the update of night population density information more accurate. The traditional machine learning and deep learning methods are difficult to effectively extract spatio-temporal information because of multi-source and heterogeneous data. In this paper, the semi-supervised DCNMF and K-Means method is used to successfully complete the spatio-temporal dimension reduction and clustering. Then, the three types of data ((taxi orders, LJ1-01 nighttime light data and land use data)) are organically combined to explain the patterns of nighttime crowd flows from different angles.
- We also analyze the crowd flows’ pattern changes in Haikou “before midnight” and “after midnight” and between downtown and citywide areas. It was found that the greater the lag in crowd flow in a certain area is (that is, the clustering of crowd flow change is “increase then decrease” or especially “decrease then increase”), the more prosperous and higher population density the area is, and the greater the land use patterns are closer to downtown residential and commercial areas. Thus, the prosperity of land use areas shows a high positive correlation with the lag of crowd flows. For every 5% increase in population density, the peak of crowd flows will be delayed by 30 min.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Land Use | Subclass |
---|---|
Commercial | business area and land for factories and mines, large industrial areas, oil fields, salt fields, quarries, as well as traffic roads, marine ports, airports and special land |
Tourism | forests, meadows, sea beaches, tourist-type rivers, POIs, hotel clusters, and other areas conducive to the growth of tourism |
Downtown Residential | the land in large, medium, and small cities and built-up areas above the county town |
Village Residential | rural settlements, arable land and ranch land that are independent of towns |
Other Areas | Sandy land, Gobi, saline land, marshland, bare land, bare rocky land, shrubland, reservoirs, and other unpopulated areas that are not suitable for developing secondary and tertiary industries |
Order 1 | Order 2 | Order 3 | Order 4 | … | |
---|---|---|---|---|---|
Order id | 175927190437 | 175928802315 | 175943512726 | 175958696894 | … |
Departure time | 19 May 2018 1:05:19 | 26 May 2018 0:02:43 | 23 July 2018 23:55:20 | 20 Sep 2018 21:49:00 | … |
Starting_lng, Starting_lat | 110.3665°, 20.0059° | 110.3249°, 20.0212° | 110.3446°, 19.9834° | 110.2913°, 20.0236° | … |
Arrive time | 19 May 2018 01:09:12 | 26 May 2018 00:04:47 | 23 July 2018 23:59:02 | 20 Sep 2018 21:53:17 | … |
Dest_lng, Dest_lat | 110.3645°, 20.0353° | 110.34629°, 20.0226° | 110.3598°, 20.0430° | 110.3740°, 20.0212° | … |
Land Use Area | Low Nighttime Light Intensity | Medium Nighttime Light Intensity | High Nighttime Light Intensity | Nighttime Population Density |
---|---|---|---|---|
Downtown residential | 16.53% | 27.94% | 55.52% | High population density (70–100%) |
Commercial | 68.21% | 21.04% | 10.75% | High population density (70–100%) |
Village residential | 88.80% | 9.91% | 1.30% | Medium population density (60–70%) |
Tourism | 96.54% | 2.97% | 0.48% | Low population density (0–60%) |
Other | 97.22% | 2.43% | 0.35% | Low population density (0–60%) |
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Han, B.; Zhu, D.; Cheng, C.; Pan, J.; Zhai, W. Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example. Remote Sens. 2022, 14, 1413. https://doi.org/10.3390/rs14061413
Han B, Zhu D, Cheng C, Pan J, Zhai W. Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example. Remote Sensing. 2022; 14(6):1413. https://doi.org/10.3390/rs14061413
Chicago/Turabian StyleHan, Bing, Daoye Zhu, Chengqi Cheng, Jiawen Pan, and Weixin Zhai. 2022. "Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example" Remote Sensing 14, no. 6: 1413. https://doi.org/10.3390/rs14061413