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Continuous risk profile and clustering-based method for investigating the effect of the automated enforcement system on urban traffic collisions

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Abstract

The automated enforcement systems (AESs), which detect speeding vehicles or vehicles that violate traffic signals, are installed and operated on urban arterial roadways to prevent traffic collisions and reduce injury severity in case of a traffic collision. As it is expensive to install the AES, it will be installed at a site where it is expected to have a great effect. However, as there are no specific guidelines on where it should be installed, it has been installed arbitrarily in high collision concentration locations (HCCLs). The objective of this study is to contribute to the improvement in the efficiency of system operation and budget execution by identifying sites where the enforcement effect of the AES is high, and providing a road manager with the basis of decision-making on system installation. This study classified road sections into clusters based on road environment characteristics and spatial collision occurrence pattern, respectively, and compared collisions statistics and occurrence pattern before and after AES installation to analyze the effect of AES installation. As a result, the study verified that the number of collisions tends to decrease at HCCLs past enforcement sites, while the number of collisions tends to increase at sites where not only red light running but also speeding is enforced after AES installation. In addition, from the perspective of collision severity, it was observed that the severity of collisions was alleviated in most clusters after AES installation. This may be viewed as a positive effect caused by the decrease in vehicle speed due to speeding enforcement.

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Acknowledgements

This research was supported by a Grant (18RDRP-B076268-05) from the R&D program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.

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Authors and Affiliations

  1. Department of Transportation Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea

    Shin Hyung Park, Shin Hyoung Park & Oh Hoon Kwon

  2. Department of Multimedia Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea

    Yunsick Sung

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  1. Shin Hyung Park
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Correspondence to Oh Hoon Kwon.

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Park, S.H., Park, S.H., Kwon, O.H. et al. Continuous risk profile and clustering-based method for investigating the effect of the automated enforcement system on urban traffic collisions. J Supercomput 75, 4350–4371 (2019). https://doi.org/10.1007/s11227-019-02752-6

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