Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches
Abstract
:1. Introduction
2. Literature Review
2.1. Built Environment and Quality of Life Related to Transportation (QoLT)
2.2. Quality of Life Related to Transportation (QoLT) and Sustainable Development
2.3. Integrating Deep Learning for Assessing Quality of Life
3. Methodology
3.1. Data Collection and Study Area
3.2. Measurement and Analysis
4. Results
4.1. Socio-Economic Profile of Participants
4.2. Built-Environment Characteristics: Sukhumvit District, Bangkok, Thailand
4.3. Satisfaction of Quality-of-Life-Related Transportation
4.4. Relationship between Built Environment and QoLT
4.5. Cluster of Built Environment and Semantic Segmentation of Road Components
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Main Variable | Sub-Variable | Details | References |
---|---|---|---|
Quality of Life | |||
Quality-of-life-related transportation (life satisfaction) | Accessibility | Access to destinations or people’s ability to reach the destinations in order to meet their needs and desire to visit or to satisfy their wants | [7,35,36,37] |
Design | Describes the physical layout of the transportation system and includes the multiple components of the system (e.g., roads, signs, and lights) | [38,39] | |
Safety | Refers to a problem caused by transportation (e.g., traffic accident) | [14,40,41,42] | |
Cost | Describes the affordability of the transportation system (e.g., travel costs) | [36,43,44] | |
Environment | Refer to the externalities or impacts caused by transportation (e.g., noise, air pollution) | [39,45,46] | |
Mobility | Describes the experience involved with the movement of people from the origin to the destination related to daily life | [10,15,47] | |
Information | Refers to information and communication of data in transportation (e.g., signage, traffic signal) | [43] | |
Built Environment | |||
Road components | Semantic segmentation of road scenes (e.g., road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain) | Percentage of observed areas (in pixels) in image. | [29,30,48] |
Land use | Land-use characteristics | Characteristics of land use that refer to urban density, diversity of activities in the area (e.g., residential, commercial, mixed-use) | [7] |
Mode of transportation | Modes | Mode choices in transportation systems (e.g., active transportation, public transportation, paratransit) | [7] |
Variables | Private Automobile | Active Transportation | Public Transit | Paratransit | Total | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Passenger Car | Motorcycle | Bus | Mass Rapid Transit | |||||||||||
n | % | n | % | n | % | n | % | n | % | n | % | n | % | |
Social Aspect | ||||||||||||||
Gender | ||||||||||||||
Male | 65 | 67 | 62 | 70.5 | 37 | 48.7 | 29 | 37.2 | 40 | 50.6 | 38 | 46.3 | 271 | 54.2 |
Female | 32 | 33 | 26 | 29.5 | 37 | 48.7 | 45 | 57.7 | 33 | 41.8 | 41 | 50 | 214 | 42.8 |
Others | 0 | 0 | 0 | 0 | 2 | 2.6 | 4 | 5.1 | 6 | 7.6 | 3 | 3.7 | 15 | 3 |
Age (years) | ||||||||||||||
18–25 | 8 | 8.2 | 7 | 8 | 16 | 21.1 | 16 | 20.5 | 29 | 36.7 | 30 | 36.6 | 106 | 21.2 |
26–35 | 46 | 47.4 | 42 | 47.7 | 34 | 44.7 | 28 | 35.9 | 35 | 44.3 | 38 | 46.3 | 223 | 44.6 |
36–59 | 36 | 37.1 | 37 | 42 | 22 | 28.9 | 29 | 37.2 | 15 | 19 | 13 | 15.9 | 152 | 30.4 |
Over 59 | 7 | 7.2 | 2 | 2.3 | 4 | 5.3 | 5 | 6.4 | 0 | 0 | 1 | 1.2 | 19 | 3.8 |
Religions | ||||||||||||||
Buddhist | 93 | 95.9 | 76 | 86.4 | 68 | 89.5 | 63 | 80.8 | 64 | 81 | 66 | 80.5 | 430 | 86 |
Christian | 2 | 2.1 | 9 | 10.2 | 5 | 6.6 | 10 | 12.8 | 9 | 11.4 | 8 | 9.8 | 43 | 8.6 |
Islam | 1 | 1 | 2 | 2.3 | 3 | 3.9 | 5 | 6.4 | 3 | 3.8 | 2 | 2.4 | 16 | 3.2 |
Others | 1 | 1 | 1 | 1.1 | 0 | 0 | 0 | 0 | 3 | 3.8 | 6 | 7.3 | 11 | 2.2 |
Marital status | ||||||||||||||
Married | 45 | 46.4 | 45 | 51.1 | 47 | 61.8 | 42 | 53.8 | 47 | 59.5 | 54 | 65.9 | 280 | 56 |
Single | 48 | 49.5 | 36 | 40.9 | 22 | 28.9 | 27 | 34.6 | 26 | 32.9 | 24 | 29.3 | 183 | 36.6 |
Divorce | 3 | 3.1 | 6 | 6.8 | 8 | 7.9 | 7 | 9 | 3 | 3.8 | 2 | 2.4 | 27 | 5.4 |
Others | 1 | 1 | 1 | 1.1 | 1 | 1.3 | 2 | 2.6 | 3 | 3.8 | 2 | 2.4 | 10 | 2 |
Economic Aspect | ||||||||||||||
Education level | ||||||||||||||
Junior high school | 5 | 5.2 | 3 | 3.4 | 5 | 6.6 | 1 | 1.3 | 0 | 0.0 | 3 | 3.7 | 17 | 3.4 |
High school | 4 | 4.1 | 10 | 11.4 | 13 | 17.1 | 12 | 15.4 | 8 | 10.1 | 13 | 15.9 | 60 | 12.0 |
Vocational college | 8 | 8.2 | 20 | 22.7 | 11 | 14.5 | 22 | 28.2 | 10 | 12.7 | 6 | 7.3 | 77 | 15.4 |
Bachelor’s degree | 67 | 69.1 | 48 | 54.5 | 44 | 57.9 | 41 | 52.6 | 55 | 69.6 | 49 | 59.8 | 304 | 60.8 |
Postgraduate | 13 | 13.4 | 7 | 8.0 | 3 | 3.9 | 2 | 2.6 | 6 | 7.6 | 11 | 13.4 | 42 | 8.4 |
Income Level (per person per month, THB) | ||||||||||||||
Less than 10,000 | 5 | 5.2 | 3 | 3.4 | 9 | 11.8 | 5 | 6.4 | 5 | 6.3 | 10 | 12.2 | 37 | 7.4 |
10,001–25,000 | 34 | 35.1 | 52 | 59.1 | 31 | 40.8 | 41 | 52.6 | 34 | 43.0 | 29 | 35.4 | 221 | 44.2 |
25,001–40,000 | 33 | 34.0 | 17 | 19.3 | 23 | 30.3 | 22 | 28.2 | 34 | 43.0 | 22 | 26.8 | 151 | 30.2 |
40,001–55,000 | 14 | 14.4 | 11 | 12.5 | 9 | 11.8 | 8 | 10.3 | 4 | 5.1 | 14 | 17.1 | 60 | 12.0 |
More than 55,000 | 11 | 11.3 | 5 | 5.7 | 4 | 5.3 | 2 | 2.6 | 2 | 2.5 | 7 | 8.5 | 31 | 6.2 |
Vadhana District | Khlong Toei District | Phra Khanong District | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
District, % | Road | Sidewalk | Building | Wall | Fence | Pole | Traffic light | Traffic sign | Vegetation | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | Motorcycle | Bicycle |
Khlong Toei | 26.86 | 1.37 | 40.39 | 0.34 | 0.7 | 0.32 | 0.01 | 0.19 | 16.13 | 0.08 | 8.63 | 1.41 | 0.05 | 2.58 | 0.1 | 0.02 | 0.06 | 0.3 | 0.05 |
Phra Khanong | 26.16 | 1.13 | 42.94 | 0.5 | 0.68 | 0.32 | 0.09 | 0.16 | 12.57 | 0.12 | 10.6 | 1.05 | 0.04 | 3.12 | 0.07 | 0.07 | 0.12 | 0.25 | 0.01 |
Vadhana | 24.08 | 1.59 | 46.79 | 0.41 | 0.47 | 0.32 | 0.01 | 0.17 | 12.51 | 0.05 | 8.23 | 1.53 | 0.05 | 3.14 | 0.14 | 0.08 | 0.06 | 0.34 | 0.03 |
Variables | Private Automobile | Active Transportation | Public Transit | Para Transit | Total | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Passenger Car | Motorcycle | Bus | Mass Rapid Transit | |||||||||||
M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | |
Accessibility | 2.67 | 0.96 | 2.69 | 0.80 | 2.95 | 0.94 | 2.77 | 0.83 | 2.73 | 0.74 | 2.64 | 0.75 | 2.74 | 0.85 |
Design | 2.69 | 1.06 | 2.70 | 0.95 | 2.98 | 1.05 | 2.71 | 0.96 | 2.41 | 0.92 | 2.41 | 0.82 | 2.65 | 0.98 |
Safety | 2.76 | 0.99 | 2.86 | 0.86 | 3.06 | 1.08 | 2.75 | 1.06 | 2.47 | 0.86 | 2.46 | 0.84 | 2.73 | 0.97 |
Cost | 2.73 | 0.95 | 2.69 | 0.87 | 2.82 | 0.90 | 2.68 | 0.93 | 2.48 | 0.83 | 2.57 | 0.81 | 2.66 | 0.89 |
Environment | 2.58 | 1.09 | 2.59 | 0.97 | 2.92 | 1.02 | 2.45 | 1.06 | 2.01 | 1.02 | 2.05 | 1.00 | 2.44 | 1.07 |
Mobility | 2.73 | 0.96 | 2.80 | 0.83 | 3.08 | 0.94 | 2.78 | 0.99 | 2.59 | 0.88 | 2.56 | 0.86 | 2.76 | 0.92 |
Information | 2.64 | 0.91 | 2.77 | 0.82 | 2.84 | 0.95 | 2.75 | 0.92 | 2.71 | 0.96 | 2.76 | 0.92 | 2.74 | 0.91 |
Main Variable | Sub-Variables | Low Satisfaction | Moderate Satisfaction | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
B | Std. Error | Wald | Exp(B) | Sig. | B | Std. Error | Wald | Exp(B) | Sig. | ||
Mode of transportation (GIS) | Pier for water transport | 0.382 | 0.612 | 0.390 | 1.466 | 0.532 | 0.753 | 0.641 | 1.382 | 2.124 | 0.240 |
Bicycle path | −0.876 | 0.445 | 3.885 | 0.416 | 0.049 * | −1.418 | 0.511 | 7.713 | 0.242 | 0.005 * | |
Bus stop | −0.389 | 0.101 | 14.859 | 0.678 | 0.000 ** | −0.395 | 0.111 | 12.624 | 0.674 | 0.000 ** | |
Elevated train station | 2.136 | 0.689 | 9.605 | 8.462 | 0.002 * | 2.721 | 0.725 | 14.089 | 15.198 | 0.000 ** | |
Subway station | −0.374 | 0.859 | 0.190 | 0.688 | 0.663 | −0.701 | 0.924 | 0.576 | 0.496 | 0.448 | |
Land-use characteristic (GIS) | Residential | 0.002 | 0.001 | 3.082 | 1.002 | 0.079 | .001 | 0.001 | 0.241 | 1.001 | 0.624 |
Commercial | −0.004 | 0.005 | 0.684 | 0.996 | 0.408 | −0.007 | 0.005 | 1.774 | 0.993 | 0.183 | |
Mixed use | −0.002 | 0.003 | 0.456 | 0.998 | 0.500 | .003 | 0.004 | .840 | 1.003 | 0.359 | |
Industry | 0.004 | 0.016 | 0.076 | 1.004 | 0.782 | −0.021 | 0.017 | 1.486 | 0.979 | 0.223 | |
Facility | 0.008 | 0.010 | 0.644 | 1.008 | 0.422 | −0.006 | 0.011 | .261 | 0.994 | 0.610 | |
Utility | −0.051 | 0.047 | 1.150 | 0.950 | 0.283 | −0.118 | 0.057 | 4.226 | 0.889 | 0.040 * | |
Recreation | 0.044 | 0.049 | 0.809 | 1.045 | 0.368 | −0.011 | 0.062 | 0.030 | 0.989 | 0.862 | |
Road components(semantic segmentation) | Road | 0.048 | 0.024 | 3.921 | 10.049 | 0.048 * | 0.031 | 0.026 | 1.493 | 1.032 | 0.222 |
Sidewalk | −0.074 | 0.113 | 0.430 | 00.928 | 0.512 | −0.119 | 0.123 | 0.933 | 0.888 | 0.334 | |
Building | −0.016 | 0.020 | 0.617 | 00.984 | 0.432 | −0.011 | 0.021 | 0.263 | 0.989 | 0.608 | |
Wall | 0.091 | 0.169 | 0.291 | 1.095 | 0.590 | 0.151 | 0.173 | 0.763 | 1.163 | 0.382 | |
Fence | 0.039 | 0.121 | 0.102 | 1.040 | 0.750 | 0.094 | 0.130 | 0.521 | 1.099 | 0.470 | |
Pole | 0.523 | 0.609 | 0.736 | 1.687 | 0.391 | 0.648 | 0.651 | 0.990 | 1.912 | 0.320 | |
Traffic light | −0.619 | 0.887 | 0.487 | 0.538 | 0.485 | −20.966 | 10.005 | 4.391 | 7.845 × 10−10 | 0.036 * | |
Traffic sign | 1.991 | 0.965 | 4.255 | 7.326 | 0.039 * | 1.764 | 0.998 | 3.122 | 5.836 | 0.077 | |
Vegetation | −0.046 | 0.024 | 3.677 | 0.955 | 0.055 | −0.038 | 0.025 | 2.369 | 0.962 | 0.124 | |
Terrain | −1.404 | 0.611 | 5.275 | 0.246 | 0.022 * | −1.084 | 0.654 | 2.751 | 0.338 | 0.097 | |
−2 Log likelihood | 814.767 | ||||||||||
Chi-square | 111.551 | ||||||||||
Significant | 0.000 | ||||||||||
McFadden | 0.115 | ||||||||||
Percent correct predicted | 59.0 |
Main Variable (Data-Gathering Techniques) | Sub-Variables | Mean Square | F | Sig. |
---|---|---|---|---|
Mode of transportation (GIS) | Pier | 0.246 | 3.149 | 0.044 * |
Bicycle path | 1.48 | 10.987 | 0.000 ** | |
Bus stop | 32.397 | 10.2 | 0.000 ** | |
Railway station | 0.048 | 4.1 | 0.017 * | |
Elevated train station | 1.282 | 13.976 | 0.000 ** | |
Subway station | 0.017 | 495 | 0.61 | |
Land-use characteristic (GIS) | Residential | 11,928,595.98 | 821.219 | 0.000 ** |
Commercial | 21,586.794 | 22.008 | 0.000 ** | |
Mixed use | 74,679.659 | 20.189 | 0.000 ** | |
Industry | 2971.661 | 25.945 | 0.000 ** | |
Facility | 2859.988 | 9.72 | 0.000 ** | |
Utility | 31.674 | 3.848 | 0.022 * | |
Recreation | 108.628 | 2.523 | 0.081 | |
Road components (semantic segmentation) | Road | 133.423 | 1.626 | 0.198 |
Sidewalk | 18.103 | 8.53 | 0.000 ** | |
Building | 366.696 | 1.815 | 0.164 | |
Wall | 2.433 | 1.789 | 0.168 | |
Fence | 11.663 | 5.3 | 0.005 ** | |
Pole | 0.04 | 0.405 | 0.667 | |
Traffic light | 0.164 | 3.131 | 0.045 * | |
Traffic sign | 0.646 | 9.5 | 0.000 ** | |
Vegetation | 3.683 | 0.033 | 0.967 | |
Terrain | 0.066 | 1.46 | 0.233 |
Variables | Cluster 1 | Cluster 2 | Cluster 3 | Total | p-Value | |
---|---|---|---|---|---|---|
Accessibility | Very dissatisfied | 0.0 | 7.1 | 21.0 | 13.0 | 0.000 ** |
Dissatisfied | 50.0 | 32.1 | 28.0 | 30.6 | ||
Medium | 50.0 | 36.4 | 37.9 | 37.2 | ||
Satisfied | 0.0 | 15.7 | 9.8 | 13.0 | ||
Very satisfied | 0.0 | 8.6 | 3.3 | 6.2 | ||
Mean | 2.67 | 2.86 | 2.57 | 2.74 | ||
Design | Very dissatisfied | 16.7 | 19.6 | 26.2 | 22.4 | 0.298 |
Dissatisfied | 33.3 | 31.1 | 27.6 | 29.6 | ||
Medium | 33.3 | 21.8 | 27.6 | 24.4 | ||
Satisfied | 16.7 | 16.1 | 11.7 | 14.2 | ||
Very satisfied | 0.0 | 11.4 | 7.0 | 9.4 | ||
Mean | 2.64 | 2.75 | 2.51 | 2.65 | ||
Safety | Very dissatisfied | 0.0 | 15.4 | 22.4 | 18.2 | 0.004 ** |
Dissatisfied | 33.3 | 28.9 | 31.3 | 30.0 | ||
Medium | 66.7 | 27.5 | 32.7 | 30.2 | ||
Satisfied | 0.0 | 15.4 | 8.9 | 12.4 | ||
Very satisfied | 0.0 | 12.9 | 4.7 | 9.2 | ||
Mean | 2.69 | 2.89 | 2.52 | 2.73 | ||
Cost | Very dissatisfied | 33.3 | 13.2 | 21.5 | 17.0 | 0.012 * |
Dissatisfied | 33.3 | 36.1 | 30.8 | 33.8 | ||
Medium | 16.7 | 25.0 | 32.2 | 28.0 | ||
Satisfied | 0.0 | 17.1 | 12.6 | 15.0 | ||
Very satisfied | 16.7 | 8.6 | 2.8 | 6.2 | ||
Mean | 2.55 | 2.76 | 2.53 | 2.66 | ||
Environment | Very dissatisfied | 33.3 | 36.4 | 43.0 | 39.2 | 0.073 |
Dissatisfied | 16.7 | 17.1 | 19.6 | 18.2 | ||
Medium | 33.3 | 21.8 | 24.3 | 23.0 | ||
Satisfied | 16.7 | 14.3 | 10.3 | 12.6 | ||
Very satisfied | 0.0 | 10.4 | 2.8 | 7.0 | ||
Mean | 2.44 | 2.57 | 2.26 | 2.44 | ||
Mobility | Very dissatisfied | 16.7 | 15.7 | 22.0 | 18.4 | 0.073 |
Dissatisfied | 0.0 | 26.1 | 26.2 | 25.8 | ||
Medium | 66.7 | 31.4 | 33.6 | 32.8 | ||
Satisfied | 16.7 | 15.7 | 14.0 | 15.0 | ||
Very satisfied | 0.0 | 11.1 | 4.2 | 8.0 | ||
Mean | 2.88 | 2.87 | 2.60 | 2.76 | ||
Information | Very dissatisfied | 33.3 | 14.6 | 25.2 | 19.4 | 0.060 |
Dissatisfied | 33.3 | 24.3 | 26.2 | 25.2 | ||
Medium | 16.7 | 34.6 | 25.2 | 30.4 | ||
Satisfied | 16.7 | 16.8 | 17.8 | 17.2 | ||
Very satisfied | 0.0 | 9.6 | 5.6 | 7.8 | ||
Mean | 2.50 | 2.85 | 2.61 | 2.74 | ||
Number of cases in each cluster | 6 (1.20%) | 280 (56.00%) | 214 (42.80%) | 500 (100%) |
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Iamtrakul, P.; Chayphong, S.; Kantavat, P.; Hayashi, Y.; Kijsirikul, B.; Iwahori, Y. Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches. Sustainability 2023, 15, 2785. https://doi.org/10.3390/su15032785
Iamtrakul P, Chayphong S, Kantavat P, Hayashi Y, Kijsirikul B, Iwahori Y. Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches. Sustainability. 2023; 15(3):2785. https://doi.org/10.3390/su15032785
Chicago/Turabian StyleIamtrakul, Pawinee, Sararad Chayphong, Pittipol Kantavat, Yoshitsugu Hayashi, Boonserm Kijsirikul, and Yuji Iwahori. 2023. "Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches" Sustainability 15, no. 3: 2785. https://doi.org/10.3390/su15032785
APA StyleIamtrakul, P., Chayphong, S., Kantavat, P., Hayashi, Y., Kijsirikul, B., & Iwahori, Y. (2023). Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches. Sustainability, 15(3), 2785. https://doi.org/10.3390/su15032785