Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016
Abstract
:1. Introduction
2. Literature Review
2.1. Space–Time Impacts of Individual Mobility
2.2. Mobility and Transport-Related Social Exclusion
3. Data and Methods
3.1. Study Area and Data
3.2. Methods
3.2.1. Activity Classification
3.2.2. Propensity Score Matching
3.2.3. Multinomial Logit Model
4. Descriptive Analyses
4.1. Residential Location and Mobility Changes of Commuters
4.2. Travel Patterns and Mobility Changes of Commuters
4.2.1. Choice of Transportation Mode
4.2.2. Comparison of Time Use among Groups of Different Travel Modes
5. Modeling and Results
6. Discussion and Policy Implications
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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2011 | 2016 | Changes (%) | |
---|---|---|---|
Population of Kunming city (ten thousand) | 648.64 | 672.8 | +1.83% |
Regional GDP (RMB 100 million) | 2510 | 4300 | +26.28% |
Built-up area (km2) | 298 | 412 | +16.06% |
Population density (person/km2) | 300.5 | 320 | +3.14% |
Population density of built-up area (person/km2) | 217.66 | 163.3 | −14.27% |
Average disposable income (RMB/year) | 21966 | 36739 | +25.16% |
Car ownership (ten thousand) | 150.8 | 226.8 | +20.13% |
Number of buses in operation | 4292 | 6397 | +19.69% |
Length of metro line (km) | 0 | 64.7 | +100.00% |
Length of road line (km) | 1642 | 1997 | +9.76% |
Length of urban expressway (km) | 119.0 | 119.2 | +0.17% |
Motor vehicle parking lot (ten thousand) | 11.0 | 32.3 | +193.64% |
Activity | Activity Classification | Description |
---|---|---|
Transportation | Travel | Actual travel of individuals participating in all activities |
Commute | Euclidean distance between home and work. It means that the traveler does not participate in other activities, but only participate in work activities | |
Subsistence Activities | Working | Work and work-related activities (e.g., business) |
Maintenance activities | Family | Outdoor activities with family (including 4 subcategories: dinner, accompanying the elderly, taking care of children and medical care) |
Shopping | Shopping alone or with family | |
Home | All activities at home | |
Leisure or Discretionary Activities | Personal | Outdoor activities completed alone (including 6 subcategories: catering, personal care, sports, social, entertainment and leisure) |
2011 | 2016 | ||
---|---|---|---|
(n = 1765) | (n = 1765) | ||
Age | 18–24 | 2.60% | 3.60% |
25–34 | 24.50% | 30.10% | |
35–44 | 37.50% | 35.70% | |
45–54 | 27.30% | 23.80% | |
55 and above | 7.90% | 6.50% | |
Gender | Male | 52.40% | 51.20% |
Female | 47.60% | 48.70% | |
Education level | Low | 6.00% | 2.20% |
middle | 58.90% | 46.80% | |
high | 35.00% | 50.90% | |
Occupation | Freelance | 9.30% | 5.40% |
Private | 22.90% | 19.60% | |
Company | 19.30% | 15.90% | |
Enterprise | 13.10% | 17.70% | |
Government | 35.20% | 41.00% | |
Family Size | 1 | 18.00% | 19.80% |
2 | 31.60% | 29.90% | |
3 | 39.40% | 40.40% | |
4 | 7.70% | 6.10% | |
5 and above | 3.10% | 3.50% | |
Family with multiple workers | 70.70% | 67.10% | |
Family with children under 6 years old | 13.30% | 15.00% | |
Family with seniors over 60 years old | 10.30% | 20.00% | |
Family with more than 1 car | 30.10% | 37.40% | |
Family has its own house | 66.80% | 64.40% | |
Income | Low | 39.70% | 31.70% |
Middle | 56.80% | 65.50% | |
High | 3.30% | 2.60% | |
Pseudo-R2 of regression = 0.098 |
Car | Transit | E-Bike | Non-Motor | |
---|---|---|---|---|
2011 | ||||
Travel | 73.49 | 92.12 | 60.83 | 42.53 |
commute | 29.43 | 42.70 | 24.96 | 14.18 |
Working | 412.83 | 391.13 | 432.55 | 301.82 |
Personal | 11.34 | 14.03 | 3.07 | 4.05 |
Family | 11.33 | 26.37 | 14.71 | 16.34 |
Shopping | 6.23 | 7.99 | 6.06 | 7.91 |
Home | 921.11 | 913.66 | 921.09 | 1065.6 |
2016 | ||||
Travel | 88.70 | 86.46 | 64.80 | 57.83 |
commute | 30.95 | 41.58 | 25.86 | 18.98 |
Working | 476.12 | 478.86 | 510.3 | 416.24 |
Personal | 10.7 | 3.89 | 6.21 | 12.34 |
Family | 7.64 | 3.96 | 1.82 | 6.67 |
Shopping | 6.15 | 3.64 | 3.22 | 4.65 |
Home | 848.84 | 850.95 | 852.91 | 941.17 |
Change | ||||
Travel | +15.21 *** | −5.66 | +3.96 ** | +15.3 *** |
commute | +1.51 | −1.12 | +0.86 ** | +4.81 *** |
Working | +63.29 ** | +87.73 *** | +77.75 *** | +114.42 *** |
Personal | −0.64 | −10.13 ** | +3.14 ** | +8.28 *** |
Family | −3.69 ** | −22.4 * | −12.9 | −9.68 |
Shopping | −0.07 | −4.36 ** | −2.84 | −3.26 |
Home | −72.28 *** | −62.71 *** | −68.17 ** | −124.42 *** |
Transit | E-Bike | Non-Motor | ||||
---|---|---|---|---|---|---|
Coef. | p | Coef. | p | Coef. | p | |
Constant | 11.244 | 0.000 *** | 13.226 | 0.000 *** | 14.601 | 0.000 *** |
(−2.751) | (0.003 **) | (−17.647) | (0.000 ***) | (0.580) | (0.493) | |
PS | 0.593 | 0.377 | 2.090 | 0.000 *** | 2.127 | 0.000 *** |
(0.866) | (0.140) | (0.533) | (0.374) | (2.501) | (0.000 ***) | |
Residential Location (ref. = Outer Suburb) | ||||||
Inner city | −14.131 | 0.000 *** | −13.891 | 0.000 *** | −13.906 | 0.000 *** |
(1.676) | (0.001 **) | (19.200) | (0.000 ***) | (1.493) | (0.000 ***) | |
Inner suburb | −14.524 | 0.000 *** | −14.299 | 0.000 *** | −14.271 | 0.124 |
(0.399) | (0.415) | (16.579) | (0.210) | (0.216) | (0.554) | |
Total travel | 0.002 | 0.397 | −0.003 | 0.340 | −0.006 | 0.079 * |
(−0.005) | (0.002 **) | (−0.004) | (0.007 **) | (−0.008) | (0.000 ***) | |
Commute | 0.035 | 0.000 *** | −0.017 | 0.001 ** | −0.083 | 0.000 *** |
(0.040) | (0.000 ***) | (−0.023) | (0.000 ***) | (−0.075) | (0.000 ***) | |
Subsistence activities | 0.001 | 0.768 | 0.001 | 0.595 | 0.000 | 0.883 |
(0.000) | (0.648) | (0.001) | (0.224) | (−0.001) | (0.089 *) | |
Maintenance activities | 0.001 | 0.536 | 0.001 | 0.686 | 0.002 | 0.536 |
(0.001) | (0.431) | (0.001) | (0.613) | (0.001) | (0.074 *) | |
Leisure or discretionary activities | −0.003 | 0.342 | −0.004 | 0.197 | −0.003 | 0.287 |
(−0.001) | (0.376) | (−0.003) | (0.222) | (−0.001) | (0.714) | |
Cases = 1765 (1765) LR chi2 = 774.524 (918.6) Cox and Snell R2 = 0.355 (0.406) Reference category = Car (Car) |
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Zheng, H.; He, B.; He, M.; Guo, J. Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016. Sustainability 2022, 14, 7672. https://doi.org/10.3390/su14137672
Zheng H, He B, He M, Guo J. Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016. Sustainability. 2022; 14(13):7672. https://doi.org/10.3390/su14137672
Chicago/Turabian StyleZheng, Hui, Baohong He, Mingwei He, and Jinghui Guo. 2022. "Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016" Sustainability 14, no. 13: 7672. https://doi.org/10.3390/su14137672