IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i18p9534-d632958.html
   My bibliography  Save this article

Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level

Author

Listed:
  • Muhan Lv

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Ningcheng Wang

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Shenjun Yao

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China)

  • Jianping Wu

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Lei Fang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China)

Abstract

As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas without congested environments. Separations need to be installed between roadways and sidewalks to improve elderly road safety.

Suggested Citation

  • Muhan Lv & Ningcheng Wang & Shenjun Yao & Jianping Wu & Lei Fang, 2021. "Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9534-:d:632958
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/18/9534/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/18/9534/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ningcheng Wang & Yufan Liu & Jinzi Wang & Xingjian Qian & Xizhi Zhao & Jianping Wu & Bin Wu & Shenjun Yao & Lei Fang, 2019. "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, MDPI, vol. 11(17), pages 1-14, August.
    2. Peng Zang & Xuhong Liu & Yabo Zhao & Hongxu Guo & Yi Lu & Charlie Q. L. Xue, 2020. "Eye-Level Street Greenery and Walking Behaviors of Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-9, August.
    3. A. Stewart Fotheringham & Martin Charlton & Chris Brunsdon, 1997. "Measuring Spatial Variations in Relationships with Geographically Weighted Regression," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 4, pages 60-82, Springer.
    4. Ye Sun & Wei Lu & Peijin Sun, 2021. "Optimization of Walk Score Based on Street Greening—A Case Study of Zhongshan Road in Qingdao," IJERPH, MDPI, vol. 18(3), pages 1-13, January.
    5. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    6. Lachapelle, Ugo & Cloutier, Marie-Soleil, 2017. "On the complexity of finishing a crossing on time: Elderly pedestrians, timing and cycling infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 54-63.
    7. Seunghoon Park & Dongwon Ko, 2020. "A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes," IJERPH, MDPI, vol. 17(9), pages 1-18, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Gálvez-Pérez & Begoña Guirao & Armando Ortuño, 2024. "Age-Friendly Urban Design for Older Pedestrian Road Safety: A Street Segment Level Analysis in Madrid," Sustainability, MDPI, vol. 16(19), pages 1-23, September.
    2. Daniel Gálvez-Pérez & Begoña Guirao & Armando Ortuño & Luis Picado-Santos, 2022. "The Influence of Built Environment Factors on Elderly Pedestrian Road Safety in Cities: The Experience of Madrid," IJERPH, MDPI, vol. 19(4), pages 1-20, February.
    3. Yiwen Zhang & Haizhi Luo & Jiami Xie & Xiangzhao Meng & Changdong Ye, 2023. "The Influence and Prediction of Built Environment on the Subjective Well-Being of the Elderly Based on Random Forest: Evidence from Guangzhou, China," Land, MDPI, vol. 12(10), pages 1-16, October.
    4. Wudong Zhao & Liwei Zhang & Xupu Li & Lixian Peng & Pengtao Wang & Zhuangzhuang Wang & Lei Jiao & Hao Wang, 2022. "Residents’ Preference for Urban Green Space Types and Their Ecological-Social Services in China," Land, MDPI, vol. 11(12), pages 1-20, December.
    5. Xiaoran Huang & Pixin Gong & Marcus White, 2022. "Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai," IJERPH, MDPI, vol. 19(13), pages 1-17, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    2. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    3. Khondoker Billah & Qasim Adegbite & Hatim O. Sharif & Samer Dessouky & Lauren Simcic, 2021. "Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017," Sustainability, MDPI, vol. 13(9), pages 1-18, May.
    4. Bo Liu & Desheng Xue & Yiming Tan, 2019. "Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China," Sustainability, MDPI, vol. 11(23), pages 1-26, December.
    5. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    6. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    7. Bae, Bumjoon & Seo, Changbeom, 2022. "Do public-private partnerships help improve road safety? Finding empirical evidence using panel data models," Transport Policy, Elsevier, vol. 126(C), pages 336-342.
    8. Svetlana BAČKALIĆ & Dragan JOVANOVIĆ & Todor BAČKALIĆ & Boško MATOVIĆ & Miloš PLJAKIĆ, 2019. "The Application Of Reliability Reallocation Model In Traffic Safety Analysis On Rural Roads," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 14(1), pages 115-125, April.
    9. Arezoo Mokhtari & Behnam Tashayo & Kaveh Deilami, 2021. "Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM 2.5 Estimation," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    10. LE GALLO, Julie, 2000. "Econométrie spatiale 2 -Hétérogénéité spatiale," LATEC - Document de travail - Economie (1991-2003) 2001-01, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    11. Izdebski, Mariusz & Jacyna-Gołda, Ilona & Gołda, Paweł, 2022. "Minimisation of the probability of serious road accidents in the transport of dangerous goods," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    12. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    13. Renfei Wu & Xunjia Zheng & Yongneng Xu & Wei Wu & Guopeng Li & Qing Xu & Zhuming Nie, 2019. "Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    14. Lv, Jinpeng & Lord, Dominique & Zhang, Yunlong & Chen, Zhi, 2015. "Investigating Peltzman effects in adopting mandatory seat belt laws in the US: Evidence from non-occupant fatalities," Transport Policy, Elsevier, vol. 44(C), pages 58-64.
    15. Ye, Wei & Xu, Yueru & Shi, Xiaomeng & Shiwakoti, Nirajan & Ye, Zhirui & Zheng, Yuan, 2024. "A macroscopic safety indicator for road segment: application of entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    16. Dereli, Mehmet Ali & Erdogan, Saffet, 2017. "A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 106-117.
    17. Maria Luisa Tumminello & Elżbieta Macioszek & Anna Granà, 2024. "Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead," Sustainability, MDPI, vol. 16(10), pages 1-33, May.
    18. Ruru Xing & Zimu Li & Xiaoyu Cai & Zepeng Yang & Ningning Zhang & Tao Yang, 2023. "Accident Rate Prediction Model for Urban Expressway Underwater Tunnel," Sustainability, MDPI, vol. 15(13), pages 1-28, July.
    19. Wang, Hwachyi & De Backer, Hans & Lauwers, Dirk & Chang, S.K.Jason, 2019. "A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)," Journal of Transport Geography, Elsevier, vol. 75(C), pages 94-109.
    20. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9534-:d:632958. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.