IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i17p4739-d262429.html
   My bibliography  Save this article

Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China

Author

Listed:
  • 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)

  • Yufan Liu

    (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)

  • Jinzi 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)

  • Xingjian Qian

    (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)

  • Xizhi Zhao

    (Research Center of Government Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, 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)

  • Bin 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)

  • 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)

  • Lei Fang

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

Abstract

The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4739-:d:262429
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/17/4739/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/17/4739/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    2. Shenjun Yao & Jinzi Wang & Lei Fang & Jianping Wu, 2018. "Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    3. Wichers, C Robert, 1975. "The Detection of Multicollinearity: A Comment," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 366-368, August.
    4. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    5. Daniel J. Graham & Stephen Glaister, 2003. "Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix," Urban Studies, Urban Studies Journal Limited, vol. 40(8), pages 1591-1607, July.
    6. David A. Belsley, 1988. "A Guide to Using the Collinearity Diagnostics," Boston College Working Papers in Economics 190, Boston College Department of Economics.
    7. Reza S. Shirazinejad & Sunanda Dissanayake & Ahmed Jalil Al-Bayati & David Daniel York, 2018. "Evaluating the Safety Impacts of Increased Speed Limits on Freeways in Kansas Using Before-And-After Study Approach," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    8. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    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. 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.
    2. 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.
    3. Hailing Xu & Jianghong Zhu & Zhanqi Wang, 2019. "Exploring the Spatial Pattern of Urban Block Development Based on POI Analysis: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(24), pages 1-25, December.

    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. Simplice Asongu & Rexon Nting, 2020. "The comparative economics of financial access in gender economic inclusion," African Journal of Economic and Management Studies, Emerald Group Publishing Limited, vol. 12(2), pages 193-207, December.
    2. Torbjørn Lorentzen, 2020. "Climate change and winter road maintenance," Climatic Change, Springer, vol. 161(1), pages 225-242, July.
    3. Asongu, Simplice A. & Biekpe, Nicholas & Cassimon, Danny, 2020. "Understanding the greater diffusion of mobile money innovations in Africa," Telecommunications Policy, Elsevier, vol. 44(8).
    4. Simplice A. Asongu & Nicholas M. Odhiambo, 2022. "The of role economic growth in modulating mobile connectivity dynamics for financial inclusion in developing countries," Working Papers 22/013, European Xtramile Centre of African Studies (EXCAS).
    5. Asongu, Simplice A. & Biekpe, Nicholas & Cassimon, Danny, 2021. "On the diffusion of mobile phone innovations for financial inclusion," Technology in Society, Elsevier, vol. 65(C).
    6. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    8. Simplice A. Asongu & Joseph Nnanna & Vanessa S. Tchamyou, 2020. "Finance, Institutions and Private Investment in Africa," Working Papers of the African Governance and Development Institute. 20/080, African Governance and Development Institute..
    9. Ahlfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2019. "The economic effects of density: A synthesis," Journal of Urban Economics, Elsevier, vol. 111(C), pages 93-107.
    10. Velandia, Margarita & Jensen, Kimberly & DeLong, Karen L. & Wszelaki, Annette & Rihn, Alicia, 2020. "Tennessee Fruit and Vegetable Farmer Preferences and Willingness to Pay for Plastic Biodegradable Mulch," Journal of Food Distribution Research, Food Distribution Research Society, vol. 51(3), November.
    11. Wang, Cheng & Wang, Gang & Guo, Ziru & Dai, Lingjun & Liu, Hongyu & Li, Yufeng & Chen, Hao & Zhao, Yongxiang & Zhang, Yanan & Cheng, Hai, 2020. "Effects of land-use change on the distribution of the wintering red-crowned crane (Grus japonensis) in the coastal area of northern Jiangsu Province, China," Land Use Policy, Elsevier, vol. 90(C).
    12. Mert Ersen & Ali Hakan Büyüklü & Semra Taşabat Erpolat, 2021. "Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods," Sustainability, MDPI, vol. 13(19), pages 1-39, October.
    13. Xingqiang Du & Jianying Weng & Quan Zeng & Yingying Chang & Hongmei Pei, 2017. "Do Lenders Applaud Corporate Environmental Performance? Evidence from Chinese Private-Owned Firms," Journal of Business Ethics, Springer, vol. 143(1), pages 179-207, June.
    14. Adham Alsharkawi & Mohammad Al-Fetyani & Maha Dawas & Heba Saadeh & Musa Alyaman, 2021. "Poverty Classification Using Machine Learning: The Case of Jordan," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    15. Asongu, Simplice A. & Agyemang-Mintah, Peter & Nting, Rexon T., 2021. "Law, mobile money drivers and mobile money innovations in developing countries," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    16. Ben Q. Liu & Dale L. Goodhue, 2012. "Two Worlds of Trust for Potential E-Commerce Users: Humans as Cognitive Misers," Information Systems Research, INFORMS, vol. 23(4), pages 1246-1262, December.
    17. Yue Zheng & Jinpei Ou & Guangzhao Chen & Xinxin Wu & Xiaoping Liu, 2022. "Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England," IJERPH, MDPI, vol. 19(10), pages 1-22, May.
    18. Qing Ye & Yi Li & Wenzhe Shen & Zhaoze Xuan, 2023. "Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    19. Simplice A. Asongu & Nicholas M. Odhiambo, 2023. "Economic sectors and globalization channels to gender economic inclusion in Sub-Saharan Africa," Working Papers of the African Governance and Development Institute. 23/020, African Governance and Development Institute..
    20. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Estimation of consumption potentiality using VIIRS night-time light data," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.

    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:11:y:2019:i:17:p:4739-:d:262429. 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.