IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v167y2025icp101-115.html
   My bibliography  Save this article

Using ZSG-DEA and inverse DEA to predict resource allocation for road traffic safety in China

Author

Listed:
  • Zhao, Ying
  • Zhu, Jiang-Hong
  • Zhang, Yan-Wei
  • Tang, Meng-Yu

Abstract

The issue of road traffic safety in China has consistently been a focal point of research within the academic community. Previous literature has proposed the use of Data Envelopment Analysis (DEA) models to evaluate safety efficiency. However, few scholars have examined the associated resource allocation issues or predicted future resource distribution. Consequently, this study aims to forecast resource allocation for future road traffic safety. We utilize road traffic safety indicators from 2012 to 2022 to calculate the resource allocation for 2025 across three aspects. Firstly, we employ the zero-sum gains Data Envelopment Analysis (ZSG-DEA) model in conjunction with the Gray Model (GM(1,1)) and the Criteria Importance Though Inter-criteria Correlation (CRITIC) method to predict the three factors influencing road traffic safety. Secondly, we utilize the inverse DEA model of frontier changes to forecast the necessary investments aligned with the planning objectives. Finally, we apply machine learning methods to predict output. The results indicate that: (1) In certain provinces, the quota for the three factors influencing road traffic safety is limited, resulting in increased pressure for future development. (2) Under the established planning objectives, some provinces require substantial investment, which should be judiciously managed. (3) Certain provinces exhibit high predicted output but must identify the underlying reasons for their low efficiency to enhance overall output. In summary, our research provides data-driven support and recommendations for decision-makers in establishing future planning goals.

Suggested Citation

  • Zhao, Ying & Zhu, Jiang-Hong & Zhang, Yan-Wei & Tang, Meng-Yu, 2025. "Using ZSG-DEA and inverse DEA to predict resource allocation for road traffic safety in China," Transport Policy, Elsevier, vol. 167(C), pages 101-115.
    • Handle: RePEc:eee:trapol:v:167:y:2025:i:c:p:101-115
    DOI: 10.1016/j.tranpol.2025.03.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X2500112X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2025.03.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Weipan & Wu, Xianhua & Chen, Jihong, 2024. "Low-carbon efficiency analysis of rail-water multimodal transport based on cross efficiency network DEA approach," Energy, Elsevier, vol. 305(C).
    2. Karambu Kiende Gatimbu & Maurice Juma Ogada & Nancy L. M. Budambula, 2020. "Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 3333-3345, April.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Lins, Marcos P. Estellita & Gomes, Eliane G. & Soares de Mello, Joao Carlos C. B. & Soares de Mello, Adelino Jose R., 2003. "Olympic ranking based on a zero sum gains DEA model," European Journal of Operational Research, Elsevier, vol. 148(2), pages 312-322, July.
    5. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    6. Ganji, S.S. & Tirkolaee, Erfan Babaee & Jahed, Rasul, 2024. "Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    7. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    8. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "Using inverse DEA and machine learning algorithms to evaluate and predict suppliers’ performance in the apple supply chain," International Journal of Production Economics, Elsevier, vol. 271(C).
    9. Ganji, S.S. & Dehghani, Alireza & Ajirlu, Shahrouz Fathi, 2024. "Evaluation of intercity road passenger transportation using a novel double-frontier game-regret-cross-efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    10. Moragues, Raul & Aparicio, Juan & Esteve, Miriam, 2023. "An unsupervised learning-based generalization of Data Envelopment Analysis," Operations Research Perspectives, Elsevier, vol. 11(C).
    11. Yang, Mian & Hou, Yaru & Ji, Qiang & Zhang, Dayong, 2020. "Assessment and optimization of provincial CO2 emission reduction scheme in China: An improved ZSG-DEA approach," Energy Economics, Elsevier, vol. 91(C).
    12. Yang, Fan & Lee, Hyoungsuk, 2022. "An innovative provincial CO2 emission quota allocation scheme for Chinese low-carbon transition," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    13. Wang, Zheng-Xin & Li, Dan-Dan & Zheng, Hong-Hao, 2020. "Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    14. Xin Zheng & Shenya Mao & Siqi Lv & Sheng Wang, 2022. "An Optimization Study of Provincial Carbon Emission Allowance Allocation in China Based on an Improved Dynamic Zero-Sum-Gains Slacks-Based-Measure Model," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    15. Qingxian An & Xuyang Liu & Yongli Li & Beibei Xiong, 2019. "Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

    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. Chu, Junfei & Hou, Tianteng & Li, Feng & Yuan, Zhe, 2024. "Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium," Energy Economics, Elsevier, vol. 134(C).
    2. Mojtaba Ghiyasi & Luyao Zhao & Weiwei Zhu, 2025. "Non-cooperative two-stage inverse DEA: a Stackelberg games approach for the efficiency analysis of China’s regional economic development and people’s living standards," Annals of Operations Research, Springer, vol. 346(3), pages 2035-2063, March.
    3. Moghaddas, Zohreh & Tosarkani, Babak Mohamadpour & Yousefi, Samuel, 2022. "Resource reallocation for improving sustainable supply chain performance: An inverse data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 252(C).
    4. Xiaoqi Zhang & Feng Yang & Fangqing Wei & Yangyang Wang, 2024. "Provincial CO2 emission efficiency analysis in China based on a game cross-efficiency approach with a fixed-sum undesirable output," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 14535-14560, June.
    5. Chu, Junfei & Dong, Yanhua & Yuan, Zhe, 2024. "An improved equilibrium efficient frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs," European Journal of Operational Research, Elsevier, vol. 318(2), pages 592-604.
    6. Andreas Dellnitz & Andreas Kleine & Wilhelm Rödder, 2018. "CCR or BCC: what if we are in the wrong model?," Journal of Business Economics, Springer, vol. 88(7), pages 831-850, September.
    7. Le, Minh Hanh & Afsharian, Mohsen & Ahn, Heinz, 2021. "Inverse Frontier-based Benchmarking for Investigating the Efficiency and Achieving the Targets in the Vietnamese Education System," Omega, Elsevier, vol. 103(C).
    8. Dalai Ma & Yaping Xiao & Na Zhao, 2022. "Optimization and Spatiotemporal Differentiation of Carbon Emission Rights Allocation in the Power Industry in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    9. Mohammad Khoveyni & Robabeh Eslami, 2022. "Merging two-stage series network structures: A DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 273-302, March.
    10. Sun, J. & Wen, W. & Wang, M. & Zhou, P., 2022. "Optimizing the provincial target allocation scheme of renewable portfolio standards in China," Energy, Elsevier, vol. 250(C).
    11. J V Guedes de Avellar & A Z Milioni & T N Rabello, 2007. "Spherical frontier DEA model based on a constant sum of inputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(9), pages 1246-1251, September.
    12. Ganji, Seyedreza Seyedalizadeh & Tirkolaee, Erfan Babaee, 2025. "Intercity Road Transportation Assessment: Double-Frontier Q-Cross-Efficiency Method," Transport Policy, Elsevier, vol. 167(C), pages 42-65.
    13. Xiaoyin Hu & Jianshu Li & Xiaoya Li & Jinchuan Cui, 2020. "A Revised Inverse Data Envelopment Analysis Model Based on Radial Models," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    14. Alireza Amirteimoori & Sohrab Kordrostami, 2012. "A distance-based measure of super efficiency in data envelopment analysis: an application to gas companies," Journal of Global Optimization, Springer, vol. 54(1), pages 117-128, September.
    15. J-L Hu & C-Y Fang, 2010. "Do market share and efficiency matter for each other? An application of the zero-sum gains data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 647-657, April.
    16. Ruchuan Zhang & Aijun Li & Davo Ayuba Dahoro, 2024. "A new approach for vehicle-health system measurement by network data envelopment analysis and an application in the USA," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 14693-14727, June.
    17. Alexandre Marinho & Simone de Souza Cardoso & Vivian Vicente de Almeida, 2009. "Avaliação da Eficiência Técnica dos Países nos Jogos Olímpicos de Pequim – 2008," Discussion Papers 1394, Instituto de Pesquisa Econômica Aplicada - IPEA.
    18. Mojtaba Ghiyasi & Ning Zhu, 2025. "Inverse data envelopment analysis for parallel production systems: an analysis in four types of Chinese banks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(1), pages 29-37, January.
    19. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    20. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(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:eee:trapol:v:167:y:2025:i:c:p:101-115. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.