IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10270-d1182181.html
   My bibliography  Save this article

Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London

Author

Listed:
  • Luís Rita

    (Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW72AZ, UK
    CycleAI, 1800-359 Lisbon, Portugal)

  • Miguel Peliteiro

    (CycleAI, 1800-359 Lisbon, Portugal)

  • Tudor-Codrin Bostan

    (CycleAI, 1800-359 Lisbon, Portugal)

  • Tiago Tamagusko

    (Research Center for Territory, Transports and Environment (CITTA), Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

  • Adelino Ferreira

    (Research Center for Territory, Transports and Environment (CITTA), Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

Abstract

Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques.

Suggested Citation

  • Luís Rita & Miguel Peliteiro & Tudor-Codrin Bostan & Tiago Tamagusko & Adelino Ferreira, 2023. "Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London," Sustainability, MDPI, vol. 15(13), pages 1-26, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10270-:d:1182181
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10270/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/10270/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, L. & Chen, C. & Srinivasan, R. & McKnight, C.E. & Ewing, R. & Roe, M., 2012. "Evaluating the safety effects of bicycle lanes in New York City," American Journal of Public Health, American Public Health Association, vol. 102(6), pages 1120-1127.
    2. Teschke, K. & Harris, M.A. & Reynolds, C.C.O. & Winters, M. & Babul, S. & Chipman, M. & Cusimano, M.D. & Brubacher, J.R. & Hunte, G. & Friedman, S.M. & Monro, M. & Shen, H. & Vernich, L. & Cripton, P., 2012. "Route infrastructure and the risk of injuries to bicyclists: A case-crossover study," American Journal of Public Health, American Public Health Association, vol. 102(12), pages 2336-2343.
    3. Dablanc, Laetitia, 2007. "Goods transport in large European cities: Difficult to organize, difficult to modernize," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 280-285, March.
    4. Ranjan Kumar Mishra & G. Y. Sandesh Reddy & Himanshu Pathak, 2021. "The Understanding of Deep Learning: A Comprehensive Review," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, 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. Pawinee Iamtrakul & Sararad Chayphong & Pittipol Kantavat & Kazuki Nakamura & Yoshitsugu Hayashi & Boonserm Kijsirikul & Yuji Iwahori, 2024. "Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images," Sustainability, MDPI, vol. 16(4), pages 1-20, February.

    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. 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.
    2. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    3. Anne C. Lusk & Walter C. Willett & Vivien Morris & Christopher Byner & Yanping Li, 2019. "Bicycle Facilities Safest from Crime and Crashes: Perceptions of Residents Familiar with Higher Crime/Lower Income Neighborhoods in Boston," IJERPH, MDPI, vol. 16(3), pages 1-21, February.
    4. Behiri, Walid & Belmokhtar-Berraf, Sana & Chu, Chengbin, 2018. "Urban freight transport using passenger rail network: Scientific issues and quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 227-245.
    5. Pedro A. P. Dias & Hugo Yoshizaki & Patricia Favero & Jose Geraldo Vidal Vieira, 2019. "Daytime or Overnight Deliveries? Perceptions of Drivers and Retailers in São Paulo City," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
    6. Yang, Chao & Chen, Mingyang & Yuan, Quan, 2021. "The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area," Journal of Transport Geography, Elsevier, vol. 92(C).
    7. Daniele Crotti & Elena Maggi, 2023. "Social Responsibility and Urban Consolidation Centres in Sustainable Freight Transport Markets," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(2), pages 829-850, July.
    8. Sandrine Ville & Jesus Gonzalez-Feliu & Laetitia Dablanc, 2010. "The limits of public policy intervention in urban logistics: The case of Vicenza (Italy) and lessons for other European cities," Post-Print halshs-00742857, HAL.
    9. Thomas Baudel & Laetitia Dablanc & Penelope Aguiar-Melgarejo & Jean Ashton, 2015. "Optimizing Urban Freight Deliveries: From Designing and Testing a Prototype System to Addressing Real Life Challenges," Post-Print hal-01255153, HAL.
    10. Nathalia de Castro Zambuzi & Cláudio Barbieri da Cunha & Edgar Blanco & Hugo T.Y. Yoshizaki & Carla D.Rosa Carvalho, 2013. "The Aspects of the Urban Distribution in a Megacity: A Comparison Between São Paulo’s and Boston’s Urban Deliveries," LARES lares_2013_858-1006-1-sm, Latin American Real Estate Society (LARES).
    11. Santos, Lui­s & Coutinho-Rodrigues, João & Current, John R., 2008. "Implementing a multi-vehicle multi-route spatial decision support system for efficient trash collection in Portugal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(6), pages 922-934, July.
    12. Bruno Durand, 2010. "E-Commerce And City Logistics: When The Sustainable Development Gets Involved In It … [E-Commerce Et Logistique Urbaine : Quand Le Developpement Durable S'En Mele…]," Post-Print hal-01770398, HAL.
    13. Jesus Gonzalez-Feliu & Bruno Durand & Dina Andriankaja, 2012. "Challenges in last-mile e-grocery urban distribution: have new B2C trends a positive impact on the environment? [Les défis du dernier kilomètre pour l'épicerie en ligne : l'impact environnemental d," Post-Print hal-01770405, HAL.
    14. Jonathan Cowie & Keith Fisken, 2023. "Urban freight policy maturity and sustainable logistics: are they related?," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-17, December.
    15. Daniele Crotti & Elena Maggi, 2017. "Urban Distribution Centres and Competition among Logistics Providers: a Hotelling Approach," SAS: Society and Sustainability 256057, Fondazione Eni Enrico Mattei (FEEM).
    16. Behrends, Sönke, 2017. "Burden or opportunity for modal shift? – Embracing the urban dimension of intermodal road-rail transport," Transport Policy, Elsevier, vol. 59(C), pages 10-16.
    17. Gilles PACHE, 2008. "Perspectives in Food e-Tailing – is Logistical Performance Always Essential to Develop a Sustainable Competitive Advantage?," Timisoara Journal of Economics, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 1(2), pages 163-176.
    18. Mathieu Gardrat & Jesus Gonzalez-Feliu & Jean-Louis Routhier, 2013. "Urban goods movement (UGM) analysis as a tool for urban planning," Post-Print halshs-00844657, HAL.
    19. Ballantyne, Erica E.F. & Lindholm, Maria & Whiteing, Anthony, 2013. "A comparative study of urban freight transport planning: addressing stakeholder needs," Journal of Transport Geography, Elsevier, vol. 32(C), pages 93-101.
    20. Orvin, Muntahith Mehadil & Fatmi, Mahmudur Rahman & Chowdhury, Subeh, 2021. "Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand," Journal of Transport Geography, Elsevier, vol. 97(C).

    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:jsusta:v:15:y:2023:i:13:p:10270-:d:1182181. 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.