Developing crash models with supporting vector machine for urban transportation planning

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Xiaoduan Sun
Subasish Das
Nicholas Broussard

Effectively incorporating roadway safety into transportation planning requires robust safety models that can quantitatively predict the safety performance of future planned roadway development options. Although various safety models have been developed including the models introduced in the first edition of Highway Safety Manual (HSM) by American Association of State Highway Transportation Officials (AASHTO), these models try to link roadway design features, such as lane with, should width, horizontal curve and vertical grade design with crash occurrences at disaggregated level and require the detailed inputting data and complex application procedures. Transportation planning mainly deals with type and functionality of roadway or roadway network. The HSM crash prediction modes for urban and suburban roadway are complex involving several sub-models for different types of collisions, which makes it hard for transportation planning applications.

This paper introduces an innovative crash prediction model with so-called Support Vector Machines (SVM). Being a branch of machine learning, SVM focuses on the recognition of patterns and regularities in data. The dramatic growth in practical applications for machine learning over the last ten years has been made possible by many important developments in the underlying algorithms, techniques and readily available open-source programming code. Motivated by lack of suitable safety models for transportation planning, this study used the SVM with crash data from Louisiana urban roadways to develop safety models for urban 2-lane roadway, multi-lane roadway and freeways with satisfactory results. Comparing with parametric statistical regression models, the SVM model produces results can not only reach the same level of accuracy but also be straightforward for practical applications in urban transportation planning.

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