Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China
Abstract
:1. Introduction
2. Literature Review
2.1. Environmental Benefits of Bike Sharing
2.2. Built Environment Factors Affecting Bike-Sharing Usage
2.3. Nonlinear Relationships between Bike-Sharing Usage and Built Environment Factors
3. Methodology
3.1. Research Framework
3.2. Dependent Variable
3.3. Independent Variables
3.3.1. Road Network Topological Variable Measurement
- 1.
- Closeness
- 2.
- Betweenness
3.3.2. Built Environment Variable Selection
3.4. Research Model and Main Algorithms
3.4.1. Gradient Boosting Decision Tree (GBDT) Algorithm
3.4.2. Partial Dependence Plots (PDPs)
4. Study Area and Data Processing
4.1. Study Area
4.2. Data Sources and Processing
4.2.1. DBS Order Data
4.2.2. Traffic GHG Emission Factor Data
4.2.3. Split Rate Data of Different Transport Modes
4.2.4. Road Network Data
4.2.5. Population Data
4.2.6. Urban POI Data
5. Results and Discussion
5.1. Model Regulation
5.2. Relative Importance of Independent Variables
5.3. Nonlinear Effects of Independent Variables
5.3.1. Road Network Topological Variables
5.3.2. Built Environment Variables
5.4. Interaction Effects between Road Network Topological Variables and Built Environment Variables
5.5. Comparison with Linear Regression
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Bus | Metro | Taxi | Car | |
---|---|---|---|---|
GHG emission coefficient (kg CO2-eq/pkm) | 0.01 | 0.015 | 0.03 | 0.034 |
Variables | Description | Data Sources | Min | Max | Mean | St. Dev |
---|---|---|---|---|---|---|
The potential GHG emission reduction of DBS trips | Transport-related GHG emission reduction caused by DBS in each study grid, kgCO2-eq | (1) DBS data of the Shenzhen data open platform (2) China products carbon footprint factors database (3) The Seventh Resident Travel Survey of Shenzhen | 0 | 2019.04 | 97.29 | 192.13 |
NQPDA5000 | The closeness within the network radius R (R = 5000 m) of each study grid, scale | (4) OpenStreetMap geographic map database | 0 | 2.96 | 1.06 | 0.63 |
TPBtA5000 | The betweenness within the network radius R (R = 5000 m) of each study grid, scale | 0 | 92.52 | 14.20 | 13.04 | |
Population density | Population per km2 in each study grid, persons/km2 | (5) WorldPop population data website | 0 | 187,516.4 | 14,205.8 | 16,343.52 |
Restaurant density | Number of restaurants per km2 in each study grid, count/km2 | (6) The urban POI data from Gaode Map | 0 | 1084 | 64.08 | 111.69 |
Life service density | Number of life services per km2 in each study grid, count/km2 | 0 | 612 | 34.16 | 64.26 | |
Commercial residence density | Number of commercial residences per km2 in each study grid, count/km2 | 0 | 864 | 23.52 | 36.81 | |
Government organization density | Number of government organizations per km2 in each study grid, count/km2 | 0 | 308 | 12.79 | 26.49 | |
Enterprise density | Number of enterprises per km2 in each study grid, count/km2 | 0 | 1824 | 116.63 | 178.29 | |
Education facility density | Number of education facilities per km2 in each study grid, count/km2 | 0 | 584 | 23.32 | 43.55 | |
Hotel facility density | Number of hotel facilities per km2 in each study grid, count/km2 | 0 | 612 | 10.03 | 25.74 | |
Sport facility density | Number of sport facilities per km2 in each study grid, count/km2 | 0 | 300 | 14.51 | 25.80 | |
Medical service density | Number of medical services per km2 in each study grid, count/km2 | 0 | 268 | 17.43 | 31.60 | |
Shopping density | Number of shopping services per km2 in each study grid, count/km2 | 0 | 88 | 4.37 | 8.21 | |
Tourist attraction density | Number of tourist attractions per km2 in each study grid, count/km2 | 0 | 152 | 1.83 | 5.80 | |
Land use mixture | Mix entropy of various types of POIs, calculated by Equation (16), scale | 0 | 2.41 | 1.13 | 0.80 | |
Bus stop density | Number of bus stops per km2 in each study grid, count/km2 | 0 | 412 | 33.38 | 49.47 | |
Distance to the nearest bus stop | Distance from the centroid of each study grid to the nearest bus stop, m | 2.67 | 3182.95 | 357.15 | 349.60 | |
Metro station density | Number of metro stations per km2 in each study grid, count/km2 | 0 | 12 | 0.25 | 1.09 | |
Distance to the nearest metro station | Distance from the centroid of each study grid to the nearest metro station, m | 15.93 | 31,163.89 | 2255.06 | 2670.99 | |
Distance to the nearest city center | Distance from the centroid of each study grid to the nearest city center, m | 43.94 | 41,058.5 | 6063.92 | 3938.47 |
Categories | Variables | Ranking | Relative Importance (%) | Sum (%) |
---|---|---|---|---|
Road network topological attributes | NQPDA5000 | 1 | 31.32 | 37.81 |
TPBtA5000 | 5 | 6.49 | ||
Population | Population density | 3 | 8.95 | 8.95 |
Land use | Restaurant density | 12 | 2.71 | 28.69 |
Life service density | 16 | 1.72 | ||
Commercial residence density | 9 | 3.01 | ||
Government Organization density | 13 | 2.53 | ||
Enterprises density | 7 | 3.98 | ||
Education facility density | 11 | 2.80 | ||
Hotel facility density | 17 | 1.53 | ||
Sport facility density | 14 | 2.41 | ||
Medical service density | 15 | 1.77 | ||
Shopping density | 19 | 1.12 | ||
Tourist attraction density | 18 | 1.22 | ||
POI mix entropy | 8 | 3.89 | ||
Transport accessibility | Bus stop density | 10 | 2.99 | 17.17 |
Distance to the nearest bus stop | 6 | 4.91 | ||
Metro station density | 20 | 0.13 | ||
Distance to the nearest metro station | 2 | 9.14 | ||
Regional location | Distance to the nearest city center | 4 | 7.38 | 7.38 |
Variables | Coefficients | Relative Importance (%) | Sum (%) |
---|---|---|---|
NQPDA5000 | 130.81 | 33.44 | 37.38 |
TPBtA5000 | −1.59 | 3.93 | |
Population density | 0.0016 | 12.66 | 12.66 |
Restaurant density | −0.12 | 2.38 | 38.40 |
Life service density | 0.09 | 2.54 | |
Commercial residence density | 0.50 | 6.15 | |
Government organization density | 0.25 | 4.39 | |
Enterprise density | −0.05 | 2.42 | |
Education facility density | 0.17 | 6.35 | |
Hotel facility density | 0.24 | 1.97 | |
Sport facility density | 0.85 | 5.04 | |
Medical service density | −0.40 | 2.34 | |
Shopping density | −0.33 | 1.39 | |
Tourist attraction density | −0.03 | 0.37 | |
POI mix entropy | −15.23 | 3.07 | |
Bus stop density | 0.21 | 3.28 | 9.26 |
Distance to the nearest bus stop | 0.05 | 2.46 | |
Metro station density | 0.83 | 0.94 | |
Distance to the nearest metro station | −0.0024 | 2.58 | |
Distance to the nearest city center | 0.0015 | 2.30 | 2.30 |
Sample size | 4260 | ||
R-squared | 0.244 |
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Zhao, J.; Yuan, C.; Mao, X.; Ma, N.; Duan, Y.; Zhu, J.; Wang, H.; Tian, B. Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China. ISPRS Int. J. Geo-Inf. 2024, 13, 287. https://doi.org/10.3390/ijgi13080287
Zhao J, Yuan C, Mao X, Ma N, Duan Y, Zhu J, Wang H, Tian B. Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China. ISPRS International Journal of Geo-Information. 2024; 13(8):287. https://doi.org/10.3390/ijgi13080287
Chicago/Turabian StyleZhao, Jiannan, Changwei Yuan, Xinhua Mao, Ningyuan Ma, Yaxin Duan, Jinrui Zhu, Hujun Wang, and Beisi Tian. 2024. "Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China" ISPRS International Journal of Geo-Information 13, no. 8: 287. https://doi.org/10.3390/ijgi13080287