Mitigating the losses of human lives owing to highway crashes needs
better insights to the roadway locations as well as the performance
of various safety measures. All these require the analysis of existing
crash data for various roadway segments, both deemed “safe” and
“unsafe” with different safety measures installed. Statistical models
project the safety standards and their suitability into the future.
Various tools and modeling techniques or regression models,
primarily based on Poisson Regression, are employed to analyze
crash data. This paper discusses some common methodological
issues which are constraining the effectiveness of various existing
models, then assesses the strengths and weaknesses of these
models, followed by introducing more promising and advanced
approaches that predict results with much more accuracy than the
traditional statistical models.
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