IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i11p1043-d81480.html
   My bibliography  Save this article

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

Author

Listed:
  • Feng Chen

    (Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

  • Xiaoxiang Ma

    (Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Suren Chen

    (Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Lin Yang

    (College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

Abstract

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

Suggested Citation

  • Feng Chen & Xiaoxiang Ma & Suren Chen & Lin Yang, 2016. "Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data," IJERPH, MDPI, vol. 13(11), pages 1-11, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1043-:d:81480
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/11/1043/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/13/11/1043/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    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. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    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. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
    2. Jinliang Xu & Tian Xin & Chao Gao & Zhenhua Sun, 2022. "Study on the Maximum Safe Instantaneous Input of the Steering Wheel against Rollover for Trucks on Horizontal Curves," IJERPH, MDPI, vol. 19(4), pages 1-23, 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. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    2. 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.
    3. Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    4. Lars Böcker & Patrick Amen & Marco Helbich, 2017. "Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands," Transportation, Springer, vol. 44(4), pages 831-852, July.
    5. Huiying Wen & Xuan Zhang & Qiang Zeng & Jaeyoung Lee & Quan Yuan, 2019. "Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data," IJERPH, MDPI, vol. 16(2), pages 1-12, January.
    6. Fabrice Gilles & Sabina Issehnane & Florent Sari, 2022. "Using short-term jobs as a way to find a regular job. What kind of role for local context?," TEPP Working Paper 2022-07, TEPP.
    7. 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.
    8. repec:hal:spmain:info:hdl:2441/dambferfb7dfprc9m052g20qh is not listed on IDEAS
    9. 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.
    10. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    11. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    12. Vipin Arora & Shuping Shi, 2016. "Nonlinearities and tests of asset price bubbles," Empirical Economics, Springer, vol. 50(4), pages 1421-1433, June.
    13. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    14. Da Fonseca José & Grasselli Martino & Ielpo Florian, 2014. "Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-37, May.
    15. Hansen, Lars Peter & Heaton, John & Luttmer, Erzo G J, 1995. "Econometric Evaluation of Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 237-274.
    16. Das, Marcel & van Soest, Arthur, 1999. "A panel data model for subjective information on household income growth," Journal of Economic Behavior & Organization, Elsevier, vol. 40(4), pages 409-426, December.
    17. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    18. Luis Garicano & Thomas N. Hubbard, 2016. "The Returns to Knowledge Hierarchies," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 32(4), pages 653-684.
    19. Yen, Steven T. & Chern, Wen S. & Lee, Hwang-Jaw, 1991. "Effects Of Income Sources On Household Food Expenditures," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271167, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    21. Bel, K. & Paap, R., 2013. "Modeling the impact of forecast-based regime switches on macroeconomic time series," Econometric Institute Research Papers EI 2013-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    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:13:y:2016:i:11:p:1043-:d:81480. 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.