IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9245-d874152.html
   My bibliography  Save this article

The Use of Macro-Level Safety Performance Functions for Province-Wide Road Safety Management

Author

Listed:
  • Paolo Intini

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh Politecnico di Bari, 70126 Bari, Italy)

  • Nicola Berloco

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh Politecnico di Bari, 70126 Bari, Italy)

  • Stefano Coropulis

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh Politecnico di Bari, 70126 Bari, Italy)

  • Roberta Gentile

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh Politecnico di Bari, 70126 Bari, Italy)

  • Vittorio Ranieri

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh Politecnico di Bari, 70126 Bari, Italy)

Abstract

Safety Performance Functions (SPFs) play a key role in identifying hotspots. Most SPFs were built at the micro-level, such as for road intersections or segments. On the other hand, in case of regional transportation planning, it may be useful to estimate SPFs at the macro-level (e.g., counties, cities, or towns) to determine ad hoc intervention prioritizations. Hence, the final aim of this study is to develop a predictive framework, supported by macro-level SPFs, to estimate crash frequencies, and consequently possible priority areas for interventions. At a province-wide level. The applicability of macro-level SPFs is investigated and tested thanks to the database retrieved in the context of a province-wide Sustainable Urban Mobility Plan (Bari, Italy). Starting from this database, the macro-areas of analysis were carved out by clustering cities and towns into census macro-zones, highlighting the potential need for safety interventions, according to different safety performance indicators (fatal + injury, fatal, pedestrian and bicycle crashes) and using basic predictors divided into geographic variables and road network-related factors. Safety performance indicators were differentiated into rural and urban, thus obtaining a set of 4 × 2 dependent variables. Then they were linked to the dependent variables by means of Negative Binomial (NB) count data models. The results show different trends for the urban and rural contexts. In the urban environment, where crashes are more frequent but less severe according to the available dataset, the increase in both population and area width leads to increasing crashes, while the increase in both road length and mean elevation are generally related to a decrease in crash occurrence. In the rural environment, the increase in population density, which was not considered in the urban context, strongly influences crash occurrence, especially leading to an increase in pedestrian and bicyclist fatal + injury crashes. The increase in the rural network length (excluding freeways) is generally related to a greater number of crashes as well. The application of this framework aims to reveal useful implications for planners and administrators who must select areas of intervention for safety purposes. Two examples of practical applications of this framework, related to safety-based infrastructural planning, are provided in this study.

Suggested Citation

  • Paolo Intini & Nicola Berloco & Stefano Coropulis & Roberta Gentile & Vittorio Ranieri, 2022. "The Use of Macro-Level Safety Performance Functions for Province-Wide Road Safety Management," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9245-:d:874152
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9245/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Jaeyoung & Abdel-Aty, Mohamed & Jiang, Ximiao, 2014. "Development of zone system for macro-level traffic safety analysis," Journal of Transport Geography, Elsevier, vol. 38(C), pages 13-21.
    2. Shahnewaz Hasanat-E-Rabbi & Omar Faruqe Hamim & Mithun Debnath & Md. Shamsul Hoque & Rich C. McIlroy & Katherine L. Plant & Neville A. Stanton, 2021. "Exploring the Relationships between Demographics, Road Safety Attitudes, and Self-Reported Pedestrian Behaviours in Bangladesh," Sustainability, MDPI, vol. 13(19), pages 1-16, September.
    3. 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.
    4. Maja Kiba-Janiak & Jarosław Witkowski, 2019. "Sustainable Urban Mobility Plans: How Do They Work?," Sustainability, MDPI, vol. 11(17), pages 1-15, August.
    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. Farida Saleem & Muhammad Imran Malik, 2022. "Safety Management and Safety Performance Nexus: Role of Safety Consciousness, Safety Climate, and Responsible Leadership," IJERPH, MDPI, vol. 19(20), pages 1-21, October.

    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. Alfonso Montella & Vittorio Marzano & Filomena Mauriello & Roberta Vitillo & Roberto Fasanelli & Mariano Pernetti & Francesco Galante, 2019. "Development of Macro-Level Safety Performance Functions in the City of Naples," Sustainability, MDPI, vol. 11(7), pages 1-21, March.
    2. 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.
    3. 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.
    4. 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.
    5. Ghadiri, Mehdi & Rassafi, Amir Abbas & Mirbaha, Babak, 2019. "The effects of traffic zoning with regular geometric shapes on the precision of trip production models," Journal of Transport Geography, Elsevier, vol. 78(C), pages 150-159.
    6. Jaroslaw Witkowski & Jakub Marcinkowski & Maja Kiba-Janiak, 2020. "A Comparative Analysis of Electronic Freight Exchanges in the United States and Europe with the Use of the Multiple Criteria Decision-Making Method “Promethee”," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 476-487.
    7. 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.
    8. Magdalena Żak & Anna Mainka, 2020. "Cross-Regional Highway Built through a City Centre as an Example of the Sustainable Development of Urban Transport," Sustainability, MDPI, vol. 12(24), pages 1-17, December.
    9. 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.
    10. 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.
    11. 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.
    12. Milhan Moomen & Amirarsalan Mehrara Molan & Khaled Ksaibati, 2023. "A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    13. Uroš Kramar & Dejan Dragan & Darja Topolšek, 2019. "The Holistic Approach to Urban Mobility Planning with a Modified Focus Group, SWOT, and Fuzzy Analytical Hierarchical Process," Sustainability, MDPI, vol. 11(23), pages 1-29, November.
    14. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    15. Gülay Demir & Milanko Damjanović & Boško Matović & Radoje Vujadinović, 2022. "Toward Sustainable Urban Mobility by Using Fuzzy-FUCOM and Fuzzy-CoCoSo Methods: The Case of the SUMP Podgorica," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    16. Oana Luca & Florian Gaman & Emanuel Răuță, 2021. "Towards a National Harmonized Framework for Urban Plans and Strategies in Romania," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    17. 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.
    18. Radoje Vujadinović & Jelena Šaković Jovanović & Aljaž Plevnik & Luka Mladenovič & Tom Rye, 2021. "Key Challenges in the Status Analysis for the Sustainable Urban Mobility Plan in Podgorica, Montenegro," Sustainability, MDPI, vol. 13(3), pages 1-28, January.
    19. Sun, Chenshuo & Pei, Xin & Hao, Junheng & Wang, Yewen & Zhang, Zuo & Wong, S.C., 2018. "Role of road network features in the evaluation of incident impacts on urban traffic mobility," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 101-116.
    20. Buckley, Cathal & Howley, Peter & Jordan, Phil, 2015. "The role of differing farming motivations on the adoption of nutrient management practices," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 4(4), July.

    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:14:y:2022:i:15:p:9245-:d:874152. 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.