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Lights, camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles

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  • Wong, Timothy

Abstract

This study estimates the effect of red light cameras (henceforth cameras) on collisions under the Los Angeles Automated Photo Enforcement Program that ran from 2006 to 2011. To control for selection bias and unobservables, a data set is constructed such that intersections with cameras are compared to control groups of nearby intersections without cameras, matched on observable characteristics. To capture potential spillover effects of cameras, control groups at various distances from the intersections with cameras are considered. A Poisson panel data model with random coefficients is applied to these data and estimated using Bayesian methods. The program suffered from weaknesses in enforcement. The city’s courts did not uphold citations and this dampened the effect cameras had on drivers. These problems are accounted for in modeling. Controlling for these concerns, results indicate that the cameras decreased red light running related collisions, but increased right-angle and injury collisions, as well as collisions overall.

Suggested Citation

  • Wong, Timothy, 2014. "Lights, camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 165-182.
  • Handle: RePEc:eee:transa:v:69:y:2014:i:c:p:165-182
    DOI: 10.1016/j.tra.2014.08.023
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    References listed on IDEAS

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    1. Burkey, Mark L. & Obeng, Kofi, 2004. "A detailed investigation of crash risk reduction resulting from red light cameras in small urban areas," MPRA Paper 36261, University Library of Munich, Germany.
    2. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    3. Chib, Siddhartha & Greenberg, Edward & Winkelmann, Rainer, 1998. "Posterior simulation and Bayes factors in panel count data models," Journal of Econometrics, Elsevier, vol. 86(1), pages 33-54, June.
    4. repec:aph:ajpbhl:2002:92:11:1822-1825_9 is not listed on IDEAS
    5. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
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    Cited by:

    1. Baratian-Ghorghi, Fatemeh & Zhou, Huaguo & Zech, Wesley C., 2016. "Red-light running traffic violations: A novel time-based method for determining a fine structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 55-65.

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