Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model
Abstract
:1. Introduction
2. Literature Review
Study | Sample (Area) | Dependent Variables | Built Environment | Method |
---|---|---|---|---|
Vergel-Tovar and Rodriguez [49] | 120 BRT stations in seven cities (Colombia, Brazil, Guatemala, and Ecuador in Latin American) | BRT ridership | Density, Diversity, Design, Destination accessibility | Factor analysis, Cluster analysis, Log-linear regression |
Li et al. [58] | 124 subway stations (Guangzhou, China) | Rail transit ridership | Density, Diversity, Station characteristics | Geographically weighted regression (GWR), K-means clustering |
Lin, Weng, Brands, Qian, and Yin [51] | 1151 TAZs in the Sixth Ring Road of Beijing (Beijing, China) | Public transport ridership | Density, Design, Diversity, Distance | Light Gradient Boosted Machine (LightGBM) |
Chakour and Eluru [59] | 8000 stops in Montreal the ridership (Montreal, Canada) | Boardings/ Alightings per hour | Design, Distance to transit, Diversity, Destination accessibility | Composite Marginal Likelihood (CML)-based ordered response probit (ORP) model |
Chen et al. [60] | Four weeks of smart card data (Nanjing, China) | Intermodal transit trips (bus and metro) | Density, Diversity, Design, Distance to transit, Destination accessibility | Traditional random forest incorporates the GWR model |
De Gruyter et al. [61] | 10,289 SA1s in Metropolitan Melbourne (Melbourne, Australia) | Commuting trips by transit/ train/ tram/bus (all modes) | Density, Diversity, Design, Destination accessibility, Distance to transit, Demand management | Ordinary least squares (OLS), Beta regression |
Yang, Xu, Rodriguez, Michael, and Zhang [46] | 75,862 older adults; 104,613 adults aged between 45 and 64 (U.S.) | Public transport trips | Design, Destination accessibility | Linear regression, Logistic regression |
Zhao et al. [62] | Approximately 3,000,000 daily card-swiping records of transit users in March 2015 (Wuhan, China) | Transit trip rates | Density, Diversity, Distance to the bus stop, Distance to the destination | Bilevel hierarchical linear model (HLM) |
Liu et al. [63] | Go card data containing trip transactions of all commuters using bus, train, and ferry services for two one-week periods (21–27 March 2016 under the old fare policy, and 20–26 March 2017 under the new fare policy) (South East Queensland, Australia) | The change in public transport ridership | Diversity, Density, Destination accessibility, Distance, | Spatial lag regression (SLR) |
Ding, Cao, Yu, and Ju [52] | 3758 commuters (Nanjing, China) | Transit commuting mode choice | Density, Diversity, Design, Destination accessibility, Distance to CBD | Semi-parametric multilevel mixed logit model |
Yu, Xie, and Chan [50] | 565 respondents from urban villages; 985 respondents from formal residences (Shenzhen, China) | Public transit choice | Density, Diversity, Distance to transit | Multinomial logistic regression (MNL) |
Pongprasert and Kubota [64] | 477 respondents (online questionnaire: 160; on-the-road survey: 317) (Bangkok, Thailand) | The probability with which car users’ switch to transit | Destination, Distance, Diversity, Density, Design, Demand management | Binary logistic regression model |
3. Data
3.1. Study Area
3.2. Data Collection
3.3. Variables
3.3.1. Characterization of Personal, Attitudinal, and Household Variables
3.3.2. Characterization of Social Environment and Built Environment Variables
- Define the centroid of each neighborhood as the origin,
- Distribute a one kilometer travel distance as a buffer to the main roads from the origin,
- Form an enclosed area with the endpoints of the acceptable travel distances in ArcGIS,
- Collect the data of the area covered by commercial facilities in the enclosed area in ArcGIS,
- Divide the data by the population of the neighborhood to obtain the commercial accessibility.
4. Method
- Regularization term: XGBoost adds the regularization term to control the complexity of the model. It helps to prevent overfitting and improve the generalization ability of the model.
- Second-order derivative: GBDT only uses the first-order derivative information of the cost function in the model training. XGBoost performs a second-order Taylor expansion on the cost function, and both the first and second derivatives can be used.
- Column sampling: The traditional GBDT uses all the data in each iteration. XGBoost uses a strategy similar to the random forest. It supports data sampling and column sampling, which not only reduces overfitting, but also reduces calculations.
- Missing value processing: The traditional GBDT is not designed to deal with missing values. XGBoost can automatically learn its splitting direction.
5. Results and Discussion
5.1. Relative Importance of Independent Variables
5.2. Non-Linear Relationships with Built Environment Variables
5.2.1. Non-Linear Relationship with the Percentage of Green Space Land Use
5.2.2. Non-Linear Relationship with the Land Use Mixture
5.2.3. Non-Linear Relationship with the Percentage of Bus-Stop Density
5.2.4. Non-Linear Relationship with the Dwelling Unit Density
5.3. Non-Linear Relationships with Key Social Environment Variable
5.4. Non-Linear Relationships with Key Personal and Household Variables
5.4.1. Non-Linear Relationships with Key Personal Variables
5.4.2. Non-Linear Relationships with Key Household Variables
5.5. Model Comparison
5.5.1. The Improvement of the XGBoost Model
5.5.2. The Performance of the XGBoost Model
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dimensions | Meaning | The Built Environment Variables Used in This Study |
---|---|---|
Density | The dwelling units or building floor area per unit of area. | Dwelling unit density (DWELLING) |
Design | The street network characteristics within an area. | Intersection density (INTERSECTION) |
Diversity | The number of different land uses in a fixed area and the represent degree | Land-use mixture (MIXTURE) |
Distance to transit | The average of the shortest street routes from the residences or workplaces in an area to the nearest rail station or bus stop | Bus-stop density (BUSSTOP) |
Destination accessibility | The ease of access to trip attractions | Commercial density (COMMERCIAL) |
Aesthetic | The attractiveness and appeal of a place | Percentage of green space land use among all land uses (GREENSPACE) |
Variable | Definition | Mean/Percentage (%) | S.D. | Min. | Max. |
---|---|---|---|---|---|
Frequency | Frequency of bus trips among older adults, trips per day, count | 0.27 | 0.73 | 0 | 6 |
Personal Variables | |||||
GENDER | 1 = Male | 60.43 | / | / | / |
0 = Female | 39.57 | / | / | / | |
AGE | Age of the respondent in years, count | 67.05 | 6.61 | 60 | 95 |
Attitudinal Variables | |||||
PROWALK | The respondent favors walking over other modes, binary, 1 = yes | 26.80 | / | 0 | 1 |
PROBIKE | The respondent favors bicycle over other modes, binary, 1 = yes | 16.49 | / | 0 | 1 |
PROEBIKE | The respondent favors e-bike over other modes, binary, 1 = yes | 6.24 | / | 0 | 1 |
PROBUS | The respondent favors bus over other modes, binary, 1 = yes | 22.89 | / | 0 | 1 |
PROMOTOR | The respondent favors motorcycle over other modes, binary, 1 = yes | 12.04 | / | 0 | 1 |
PROCAR | The respondent favors car over other modes, binary, 1 = yes | 2.75 | / | 0 | 1 |
Household Variables | |||||
HHSIZE_1 | Household size is one person, binary, 1 = yes | 19.89 | / | 0 | 1 |
HHSIZE_2 | Household size is two persons, binary, 1 = yes | 35.34 | / | 0 | 1 |
HHSIZE > 2 | Household size is three or more persons, binary, 1 = yes | 44.77 | / | 0 | 1 |
HIGHINC | High household income (>60,000 RMB/year), binary, 1 = yes | 15.22 | / | 0 | 1 |
MEDINC | Medium household income (20,000–60,000 RMB/year), binary, 1 = yes | 47.82 | / | 0 | 1 |
LOWINC | Low household income (<20,000 RMB/year), binary, 1 = yes | 36.96 | / | 0 | 1 |
BUSDIST | Distance from home to the nearest bus-stop (km), continuous | 0.5 | 0.36 | 0.1 | 1.2 |
BIKES | Number of bikes in a household, count | 0.61 | 0.71 | 0 | 5 |
E-BIKES | Number of electric bikes in a household, count | 0.22 | 0.46 | 0 | 4 |
MOTORS | Number of motorcycles in a household, count | 0.76 | 0.85 | 0 | 5 |
CARS | Number of private cars in a household, count | 0.17 | 0.44 | 0 | 4 |
Social Environment Variables | |||||
P_ELDERLY | Proportions of older adults in a neighborhood, continuous | 0.14 | 0.06 | 0.01 | 0.29 |
P_HIGHINC | Proportions of high-income households in a neighborhood, continuous | 15.64 | / | 0 | 1 |
P_MEDINC | Proportions of medium-income households in a neighborhood, continuous | 61.21 | / | 0 | 1 |
P_LOWINC | Proportions of low-income households in a neighborhood, continuous | 23.15 | / | 0 | 1 |
Built Environment Variables | |||||
DWELLING | Dwelling units’ density, 1000 units/km2, continuous | 3.34 | 4.32 | 0.02 | 17.42 |
INTERSECTION | Intersection density, number of intersections per km2, continuous | 2.79 | 3.18 | 0 | 13.26 |
MIXTURE | Land-use mixture, Entropy Index, continuous | 0.7 | 0.98 | 0 | 1 |
COMMERCIAL | Area coverage of commercial establishments within 1 km from the center of a neighborhood, in ha, continuous | 33.19 | 33.08 | 0 | 230.46 |
BUSSTOP | Bus-stop density, number of bus stops per km2, continuous | 0.7 | 0.18 | 0 | 1 |
GREENSPACE | Percentage of green space land use among all land uses, continuous | 0.07 | 0.08 | 0 | 0.65 |
Independent Variables | F Score | Relative Importance (%) | Ranking |
---|---|---|---|
Personal Variables | 15.56% | ||
f0(GENDER) | 136 | 2.61% | 13 |
f1(AGE) | 676 | 12.96% | 1 |
Attitudinal Variables | 6.59% | ||
f2(PROWALK) | 101 | 1.81% | 17 |
f3(PROBIKE) | 9 | 0.16% | 28 |
f4(PROEBIKE) | 8 | 0.14% | 29 |
f5(PROBUS) | 151 | 2.71% | 12 |
f6(PROMOTOR) | 59 | 1.06% | 20 |
f7(PROCAR) | 39 | 0.70% | 24 |
Household Variables | 26.46% | ||
f8(HHSIZE_1) | 53 | 0.95% | 21 |
f9(HHSIZE_2) | 74 | 1.33% | 18 |
f10(HHSIZE > 2) | 102 | 1.83% | 16 |
f11(HIGHINC) | 135 | 2.43% | 14 |
f12(MEDINC) | 130 | 2.34% | 15 |
f13(LOWINC) | 43 | 0.77% | 23 |
f14(BUSDIST) | 327 | 5.87% | 8 |
f15(BIKES) | 198 | 3.56% | 11 |
f16(E-BIKES) | 66 | 1.19% | 19 |
f17(MOTORS) | 300 | 5.39% | 9 |
f18(CARS) | 45 | 0.81% | 22 |
Social Environment Variables | 10.38% | ||
f19(P_ELDERLY) | 516 | 9.27% | 2 |
f20(P_HIGHINC) | 17 | 0.31% | 26 |
f21(P_MEDINC) | 33 | 0.59% | 25 |
f22(P_LOWINC) | 12 | 0.22% | 27 |
Built Environment Variables | 41.97% | ||
f23(DWELLING) | 384 | 6.90% | 6 |
f24(INTERSECTION) | 351 | 6.31% | 7 |
f25(MIXTURE) | 440 | 7.91% | 4 |
f26(COMMERCIAL) | 267 | 4.80% | 10 |
f27(BUSSTOP) | 397 | 7.13% | 5 |
f28(GREENSPACE) | 497 | 8.93% | 3 |
Model | XGBoost | Multi-Linear Regression | |
---|---|---|---|
Metrix | |||
R² | 0.838 | 0.160 |
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Wang, L.; Zhao, C.; Liu, X.; Chen, X.; Li, C.; Wang, T.; Wu, J.; Zhang, Y. Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model. Int. J. Environ. Res. Public Health 2021, 18, 9592. https://doi.org/10.3390/ijerph18189592
Wang L, Zhao C, Liu X, Chen X, Li C, Wang T, Wu J, Zhang Y. Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model. International Journal of Environmental Research and Public Health. 2021; 18(18):9592. https://doi.org/10.3390/ijerph18189592
Chicago/Turabian StyleWang, Lanjing, Chunli Zhao, Xiaofei Liu, Xumei Chen, Chaoyang Li, Tao Wang, Jiani Wu, and Yi Zhang. 2021. "Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model" International Journal of Environmental Research and Public Health 18, no. 18: 9592. https://doi.org/10.3390/ijerph18189592