Hotspot identification by a binary model: an approach to mitigate road accidents

• Type: Chapter or Part of a Book
• Date: 2013

Authors

António Fidalgo Couto (Author) FEUP; Sara Ferreira (Author) FEUP

Book

Title: Advances in Engineering Research

Pages: 207-221

ISBN: 978-1-62618-982-9

Scientific classification

FOS: Engineering and technology > Civil engineering

CORDIS: Technological sciences > Engineering

Other information

Abstract (EN): This chapter presents an alternative approach to research regarding hotspot definition and identification based on a probabilistic model that defines the dependent variable as an indicator of a discrete choice. A binary choice model was used considering a binary dependent variable that differentiates a hotspot (category 1) from a safe (category 0) site set by the number of accidents per kilometer. The proposed methodology consists of two main steps. First, a threshold value for the number of accidents is set to distinguish hotspots from safe sites (category 1 or 0, respectively). A strategy to more accurately define a hot spot was developed based on an epidemiological criteria constraint. Based on this classification, a binary model is applied that allows the construction of an ordered site list using the probability of a site being a hotspot. To apply this approach risk factors including traffic volume, the number of minor intersections per kilometer, the road function classification and land use from an urban segment data set collected over a five-year period from Porto, Portugal, were used. The probabilities were estimated by the binary choice model, and a performance evaluation was then applied. The second step involves the definition of four classes to classify a site in terms of safety using the uncertainty in a site being a hot spot, setting for each class a range of probability values. Thus, the classification of a site through one of these classes may be a tool to define priorities and types of engineering intervention, allowing for the efficient management of site intervention. The gains of using this method are related to the simplicity of its application, while critical issues such as prior distribution effect assumptions and the regression-to-the-mean phenomenon are overcome. Further, the proposed model provides a realistic and intuitive perspective and supports easy practical application. Once a site is identified, the diagnosis of the problems and potential treatments will be straightforward.

Language: English

Type (Professor's evaluation): Scientific

Notes: Chapter 7 2013 Nova SciencePublishers,inc.

No. of pages: 14