Identification of Intersections with Promise for Red Light Camera Safety Improvement: Application of Generalized Estimating Equations and Empirical Bayes

Red light cameras can be used as an alternative tool to supplement police efforts in enforcement against red light running, which is a major contributing factor to vehicle collisions at signalized intersections. The decision to install red light cameras should ensure overall improvement in traffic safety at signalized intersections. The major problem is deciding where and when to install the cameras in order to achieve the safety benefit. Therefore, guidelines are necessary for identifying and priority-ranking those intersections that have promise as sites for potential safety improvement. The primary objective of this study is to develop a safety analysis tool for estimating the safety impact of the installation of red light cameras at signalized intersections. Moreover, this research study provides a tool for identifying and priority-ranking problem intersections with respect to red light running within the entire roadway network under the jurisdiction of a particular agency. The proposed approach uses the empirical Bayes method, collision prediction models, and collision modification factors to estimate the safety changes upon installation of a red light camera at a signalized intersection. The generalized estimating equations technique with the assumption of negative binomial error distribution was used for development of the collision prediction models.

Language

  • English

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Filing Info

  • Accession Number: 01044777
  • Record Type: Publication
  • ISBN: 9780309104463
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 8 2007 6:08PM