Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. Elderly Pedestrian Collision and Road Network Data
2.2.2. Exposure Data
2.2.3. Built Environment Data
3. Method
3.1. Variable Selection
3.2. Global Collision Prediction Model—Poisson Regression
3.3. Local Collision Prediction Model—Geographically Weighted Poisson Regression
3.4. Measures of Goodness of Fit
3.5. Measure of Spatial Nonstationarity—Moran’s I
4. Results and Discussion
4.1. Global Model–Poisson Regression
4.2. Local Model—Geographically Weighted Poisson Regression
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- United Nations. World Population Prospects 2019: Highlights; United Nations: New York, NY, USA, 2019. [Google Scholar]
- National Bureau of Statistics of China. Seventh National Census Bulletin (No.5). Available online: http://www.gov.cn/xinwen/2021-05/11/content_5605787.htm (accessed on 6 August 2021). (In Chinese)
- World Health Organization. Global Health and Aging; World Health Organization: Geneva, Switzerland, 2011. [Google Scholar]
- Park, S.; Ko, D. A multilevel model approach for investigating individual accident characteristics and neighborhood environment characteristics affecting pedestrian-vehicle crashes. Int. J. Environ. Res. Public Health 2020, 17, 3107. [Google Scholar] [CrossRef]
- Keall, M.D. Pedestrian exposure to risk of road accident in New-Zealand. Accid. Anal. Prev. 1995, 27, 729–740. [Google Scholar] [CrossRef]
- Oxley, J.; Fildes, B.; Ihsen, E.; Charlton, J.; Day, R. Differences in traffic judgements between young and old adult pedestrians. Accid. Anal. Prev. 1997, 29, 839–847. [Google Scholar] [CrossRef]
- McDowd, J.M.; Craik, F.I.M. Effects of aging and task-difficulty on divided attention performance. J. Exp. Psychol. Hum. Percept. Perform. 1988, 14, 267–280. [Google Scholar] [CrossRef] [PubMed]
- McKnight, A.J. Driver and pedestrian training. In Transportation in an Aging Society: Improving Mobility and Safety for Older Persons; Transportation Research Board, National Research Council: Washington, DC, USA, 1988; pp. 101–133. [Google Scholar]
- Zegeer, C.V.; Stutts, J.C.; Huang, H.; Zhou, M.; Rodgman, E. Analysis of elderly pedestrian accidents and recommended countermeasures. In Pedestrian, Bicycle, and Older Driver Research: Operations and Safety; National Academy Press: Washington, DC, USA, 1993; pp. 56–63. [Google Scholar]
- Lam, W.W.Y.; Yao, S.; Loo, B.P.Y. Pedestrian exposure measures: A time-space framework. Travel Behav. Soc. 2014, 1, 22–30. [Google Scholar] [CrossRef]
- Lee, S.; Yoon, J.; Woo, A. Does elderly safety matter? Associations between built environments and pedestrian crashes in Seoul, Korea. Accid. Anal. Prev. 2020, 144, 105621. [Google Scholar] [CrossRef]
- Abdel-Aty, M.A.; Chen, C.L.; Radwan, A.E. Using conditional probability to find driver age effect in crashes. J. Transp. Eng. 1999, 125, 502–507. [Google Scholar] [CrossRef]
- Organization for Economic Cooperation and Development (OECD). Ageing and Transport: Mobility Needs and Safety Issues; Organization for Economic Cooperation and Development (OECD): Paris, France, 2001. [Google Scholar]
- Staplin, L.; Lococo, K.; Byington, S.; Harkey, D. Highway Design Handbook for Older Drivers and Pedestrians; The Federal Highway Administration: Washington, DC, USA, 2001.
- Kim, D. The transportation safety of elderly pedestrians: Modeling contributing factors to elderly pedestrian collisions. Accid. Anal. Prev. 2019, 131, 268–274. [Google Scholar] [CrossRef] [PubMed]
- Lachapelle, U.; Cloutier, M.-S. On the complexity of finishing a crossing on time: Elderly pedestrians, timing and cycling infrastructure. Transp. Res. Pt. A Policy Pract. 2017, 96, 54–63. [Google Scholar] [CrossRef]
- Hadayeghi, A.; Shalaby, A.S.; Persaud, B.N. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accid. Anal. Prev. 2010, 42, 676–688. [Google Scholar] [CrossRef]
- Mukherjee, D.; Mitra, S. Impact of road infrastructure land use and traffic operational characteristics on pedestrian fatality risk: A case study of Kolkata, India. Transp. Dev. Econ. 2019, 5, 1–9. [Google Scholar] [CrossRef]
- Lee, J.; Gim, T.T. A spatial econometrics perspective on the characteristics of urban traffic accidents: Focusing on elderly drivers’ accidents in Seoul, South Korea. Int. J. Inj. Control. Saf. Promot. 2020, 27, 520–527. [Google Scholar] [CrossRef]
- Shanghai Statistics Bureau. Main Data Bulletin of the Seventh National Population Census in Shanghai (No.1). Available online: http://tjj.sh.gov.cn/tjgb/20210517/cc22f48611f24627bc5ee2ae96ca56d4.html (accessed on 6 August 2021). (In Chinese)
- Yao, S.; Loo, B.P.; Lam, W.W. Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: Space-time path vs. potential path tree methods. Accid. Anal. Prev. 2015, 75, 320–332. [Google Scholar] [CrossRef] [PubMed]
- Qin, X.; Ivan, J.N. Estimating pedestrian exposure prediction model in rural areas. In 2001 Trb Distinguished Lecture, Pt 1—Bicycle and Pedestrian Research, Pt 2—Safety and Human Performance; Transportation Research Board: Washington, DC, USA, 2001; pp. 89–96. [Google Scholar]
- Davis, D.G.; Braaksma, J.P. Adjusting for luggage-laden pedestrains in airport terminals. Transp. Res. Pt. A Policy Pract. 1988, 22, 375–388. [Google Scholar] [CrossRef]
- Ukkusuri, S.; Miranda-Moreno, L.F.; Ramadurai, G.; Isa-Tavarez, J. The role of built environment on pedestrian crash frequency. Saf. Sci. 2012, 50, 1141–1151. [Google Scholar] [CrossRef]
- Wier, M.; Weintraub, J.; Humphreys, E.H.; Seto, E.; Bhatia, R. An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accid. Anal. Prev. 2009, 41, 137–145. [Google Scholar] [CrossRef] [PubMed]
- Wang, N.C.; Liu, Y.F.; Wang, J.Z.; Qian, X.J.; Zhao, X.Z.; Wu, J.P.; Wu, B.; Yao, S.J.; Fang, L. Investigating the potential of using POI and Nighttime Light data to map urban road safety at the micro-level: A case in Shanghai, China. Sustainability 2019, 11, 4739. [Google Scholar] [CrossRef] [Green Version]
- Dai, D.J.; Jaworski, D. Influence of built environment on pedestrian crashes: A network-based GIS analysis. Appl. Geogr. 2016, 73, 53–61. [Google Scholar] [CrossRef]
- OpenStreetMap. Available online: https://www.openstreetmap.org/#map=11/22.3567/114.1363 (accessed on 6 August 2021).
- Zang, P.; Liu, X.; Zhao, Y.; Guo, H.; Lu, Y.; Xue, C.Q.L. Eye-level street greenery and walking behaviors of older adults. Int. J. Environ. Res. Public Health 2020, 17, 6130. [Google Scholar] [CrossRef]
- Sun, Y.; Lu, W.; Sun, P. Optimization of walk score based on street greening-A case study of Zhongshan Road in Qingdao. Int. J. Environ. Res. Public Health 2021, 18, 1277. [Google Scholar] [CrossRef] [PubMed]
- Panorama Static Image API. Available online: https://lbsyun.baidu.com/index.php?title=viewstatic (accessed on 6 August 2021).
- Zhao, H.; Shi, J.; Qi, X.; Wang, X.; Jia, J. Pyramid scene parsing network. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Honolulu, HI, USA, 21–26 July 2017; pp. 6230–6239. [Google Scholar]
- Zhou, B.; Zhao, H.; Puig, X.; Fidler, S.; Barriuso, A.; Torralba, A. Scene Parsing through ADE20K Dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); IEEE Computer Society: Honolulu, HI, USA; 21–26 July 2017, July; pp. 5122–5130.
- Baidu Static Map API. Available online: https://lbsyun.baidu.com/index.php?title=static (accessed on 6 August 2021).
- FangTianXia. Available online: http://sh.fang.com (accessed on 6 August 2021).
- Loo, B.P.Y. The identification of hazardous road locations: A comparison of the Blacksite and hot zone methodologies in Hong Kong. Int. J. Sustain. Transp. 2009, 3, 187–202. [Google Scholar] [CrossRef]
- Loo, B.P.Y.; Yao, S. The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment. Comput. Environ. Urban. Syst. 2013, 41, 249–261. [Google Scholar] [CrossRef]
- O’Brien, R.M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 2007, 41, 673–690. [Google Scholar] [CrossRef]
- Sonnberger, H. Regression diagnostics—Identifying influential data and sources of collinearity. J. Appl. Econom. 1989, 4, 97–99. [Google Scholar] [CrossRef]
- Abdel-Aty, M.A.; Radwan, A.E. Modeling traffic accident occurrence and involvement. Accid. Anal. Prev. 2000, 32, 633–642. [Google Scholar] [CrossRef]
- Wang, X.S.; Fan, T.X.; Chen, M.; Deng, B.; Wu, B.; Tremont, P. Safety modeling of urban arterials in Shanghai, China. Accid. Anal. Prev. 2015, 83, 57–66. [Google Scholar] [CrossRef]
- Lord, D.; Mannering, F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp. Res. Pt. A Policy Pract. 2010, 44, 291–305. [Google Scholar] [CrossRef] [Green Version]
- Fotheringham, A.S.; Brunsdon, C.; Charlton, M. Geographically Weighted Regression; The Analysis of Spatially Varying Relationships; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2002. [Google Scholar]
- Fotheringham, A.S.; Charlton, M.; Brunsdon, C. Measuring spatial variations in relationships with geographically weighted regression. In Recent Developments in Spatial Analysis; Springer: Berlin, Germany, 1997. [Google Scholar]
- Brunsdon, C.; Fotheringham, A.S.; Charlton, M.E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 1998, 28, 281–298. [Google Scholar] [CrossRef]
- Pirdavani, A.; Bellemans, T.; Brijs, T.; Wets, G. Application of geographically weighted regression technique in spatial analysis of fatal and injury crashes. J. Transp. Eng. 2014, 140, 04014032. [Google Scholar] [CrossRef]
- Nakaya, T. GWR4.09 User Manual. Available online: https://sgsup.asu.edu/sites/default/files/SparcFiles/gwr4manual_409.pdf (accessed on 6 August 2021).
- Cliff, A.D.; Ord, J.K. Spatial Processes: Models and Applications; Pion: London, UK, 1981. [Google Scholar]
- Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef] [PubMed]
- Li, H.F.; Calder, C.A.; Cressie, N. Beyond Moran’s I: Testing for spatial dependence based on the spatial autoregressive model. Geogr. Anal. 2007, 39, 357–375. [Google Scholar] [CrossRef]
- Pei, X.; Wong, S.C.; Sze, N.N. The roles of exposure and speed in road safety analysis. Accid. Anal. Prev. 2012, 48, 464–471. [Google Scholar] [CrossRef] [PubMed]
- Duan, J.; Wang, Y.; Fan, C.; Xia, B.; de Groot, R. Perception of urban environmental risks and the effects of Urban Green Infrastructures (UGIs) on human well-being in four public green spaces of Guangzhou, China. Environ. Manag. 2018, 62, 500–517. [Google Scholar] [CrossRef] [PubMed]
Variable | Description | Max | Min | Avg | SD | VIF |
---|---|---|---|---|---|---|
Dependent Variable | ||||||
elder_collision | No. of elderly pedestrian collisions | 7 | 0 | 0.333 | 0.8076 | - |
Exposure Variable | ||||||
vehicle_km | Vehicle kilometers travelled by taxies | 1,220,000 | 0 | 120,000 | 153,000 | 2.072 |
num_elder | No. of persons aged 60 or above within 500 m buffer | 63.577 | 0.1 | 23.953 | 12.859 | 2.271 |
Roadway Variable | ||||||
p_green | Average proportion of green space | 0.690 | 0 | 0.230 | 0.143 | 1.815 |
p_sky | Average proportion of sky space | 0.638 | 0 | 0.342 | 0.130 | 1.686 |
p_building | Average proportion of building space | 0.610 | 0 | 0.171 | 0.101 | 1.101 |
p_sidewalk | Average proportion of sidewalk space | 0.112 | 0 | 0.029 | 0.018 | 1.631 |
speed_limit | Speed limit | 60 | 40 | 44.602 | 7.602 | 1.643 |
num_junction | No. of road junctions | 3 | 0 | 0.762 | 0.639 | 2.792 |
rd_width | Road width | 21 | 3 | 6.896 | 2.676 | 1.153 |
Community Variable | ||||||
num_nursing | No. of nursing homes within 500 m buffer | 4 | 0 | 0.663 | 0.879 | 1.258 |
num_school | No. of schools within 500 m buffer | 10 | 0 | 2.441 | 2.383 | 1.402 |
num_station | No. of bus stops and metro stations within 500 m buffer | 79 | 0 | 28.621 | 16.080 | 3.600 |
num_medical | No. of hospitals and clinics within 500 m buffer | 10 | 0 | 2.074 | 1.851 | 3.129 |
num_market | No. of traditional markets and supermarkets within 500 m buffer | 34 | 0 | 7.416 | 7.030 | 2.487 |
num_park | No. of parks and squares within 500 m buffer | 4 | 0 | 0.579 | 0.848 | 1.254 |
area_green | Area of green land (sq. m) | 198,000 | 0 | 29,031 | 28,423 | 1.087 |
area_residential | Area of residential land (sq. m) | 755,000 | 0 | 395,000 | 194,000 | 2.438 |
area_commercial | Area of commerical land (sq. m) | 326,000 | 0 | 24,851 | 50,169 | 2.035 |
avg_price | Average house price (RMB Yuan) within 500 m buffer | 165,210 | 0 | 42,731 | 17,245 | 1.743 |
Variable | Coef. | Robust Std. Err. | z | p > |z| | 95% Confidence Interval | |
---|---|---|---|---|---|---|
vehicle_km | 0.173 | 0.064 | 2.720 | 0.007 *** | 0.048 | 0.298 |
num_elder | 0.309 | 0.086 | 3.580 | <0.001 *** | 0.140 | 0.478 |
p_green | −0.316 | 0.144 | −2.200 | 0.028 ** | −0.598 | −0.035 |
p_sky | −0.014 | 0.127 | −0.110 | 0.910 | −0.263 | 0.235 |
p_building | −0.146 | 0.094 | −1.560 | 0.118 | −0.329 | 0.037 |
p_sidewalk | 0.236 | 0.085 | 2.800 | 0.005 *** | 0.071 | 0.402 |
speed_limit | 0.064 | 0.079 | 0.810 | 0.416 | −0.091 | 0.219 |
num_junction | 0.380 | 0.057 | 6.670 | 0.000 *** | 0.268 | 0.492 |
rd_width | 0.088 | 0.065 | 1.350 | 0.177 | −0.040 | 0.215 |
num_nursing | 0.158 | 0.059 | 2.660 | 0.008 *** | 0.041 | 0.274 |
num_school | 0.152 | 0.077 | 1.970 | 0.049 ** | 0.001 | 0.302 |
num_station | 0.155 | 0.068 | 2.270 | 0.023 ** | 0.021 | 0.289 |
num_medical | −0.136 | 0.081 | −1.690 | 0.091 * | −0.294 | 0.022 |
num_market | 0.164 | 0.079 | 2.060 | 0.039 ** | 0.008 | 0.319 |
num_park | 0.001 | 0.070 | 0.020 | 0.987 | −0.136 | 0.138 |
area_green | −0.134 | 0.094 | −1.430 | 0.153 | −0.317 | 0.050 |
area_residential | 0.441 | 0.113 | 3.910 | <0.001 *** | 0.220 | 0.662 |
area_commercial | −0.034 | 0.071 | −0.470 | 0.635 | −0.174 | 0.106 |
avg_price | −0.122 | 0.132 | −0.920 | 0.358 | −0.381 | 0.138 |
_cons | −1.550 | 0.085 | −18.250 | <0.001 *** | −1.717 | −1.384 |
AIC: 1749.306; BIC: 1851.634 |
Model | MAD | MSPE | NRMSE |
---|---|---|---|
Poisson | 0.536 | 0.761 | 0.383 |
GWPR | 0.366 | 0.395 | 0.170 |
Variable | Min | Max | Lower Quartile | Median | Upper Quartile |
---|---|---|---|---|---|
vehicle_km | −0.588 | 1.400 | −0.120 | 0.274 | 0.400 |
num_elder | −0.268 | 1.249 | 0.180 | 0.293 | 0.483 |
p_green | −1.348 | 2.992 | −0.737 | 0.190 | 0.084 |
p_sky | −0.750 | 4.105 | −0.290 | 0.072 | 0.206 |
p_building | −0.726 | 2.307 | −0.279 | −0.095 | 0.020 |
p_sidewalk | −0.203 | 0.925 | 0.019 | 0.117 | 0.316 |
speed_limit | −0.381 | 0.551 | −0.111 | 0.025 | 0.169 |
num_junction | 0.165 | 0.765 | 0.292 | 0.374 | 0.486 |
rd_width | −0.351 | 0.482 | −0.115 | −0.001 | 0.179 |
num_nursing | −0.076 | 0.693 | 0.124 | 0.211 | 0.410 |
num_school | −0.470 | 0.874 | −0.078 | 0.185 | 0.409 |
num_station | −0.462 | 0.631 | −0.073 | 0.182 | 0.314 |
num_medical | −0.804 | 0.451 | −0.477 | −0.094 | 0.104 |
num_market | −0.695 | 0.890 | −0.213 | 0.219 | 0.465 |
num_park | −0.925 | 0.598 | −0.213 | −0.043 | 0.154 |
area_green | −0.859 | 0.564 | −0.366 | −0.135 | 0.054 |
area_residential | −0.006 | 1.176 | 0.480 | 0.611 | 0.775 |
area_commercial | −0.683 | 0.612 | −0.136 | −0.022 | 0.128 |
avg_price | −1.167 | 0.839 | −0.451 | −0.017 | 0.387 |
Intercept | −3.688 | −1.433 | −2.480 | −1.885 | −1.701 |
AIC: 1076.787; BIC: 1785.385 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lv, M.; Wang, N.; Yao, S.; Wu, J.; Fang, L. Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level. Int. J. Environ. Res. Public Health 2021, 18, 9534. https://doi.org/10.3390/ijerph18189534
Lv M, Wang N, Yao S, Wu J, Fang L. Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level. International Journal of Environmental Research and Public Health. 2021; 18(18):9534. https://doi.org/10.3390/ijerph18189534
Chicago/Turabian StyleLv, Muhan, Ningcheng Wang, Shenjun Yao, Jianping Wu, and Lei Fang. 2021. "Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level" International Journal of Environmental Research and Public Health 18, no. 18: 9534. https://doi.org/10.3390/ijerph18189534