The Identification of Intersection Entrance Accidents Based on Autoencoder

Du, Yingcui; Sun, Feng; Jiao, Fangtong; Liu, Benxing; Wang, Xiaoqing; Zhao, Pengsheng

doi:10.3390/su15118533

Open AccessArticle

The Identification of Intersection Entrance Accidents Based on Autoencoder

by

Yingcui Du

,

Feng Sun

^*

,

Fangtong Jiao

^*

,

Benxing Liu

,

Xiaoqing Wang

and

Pengsheng Zhao

Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China

^*

Authors to whom correspondence should be addressed.

Sustainability 2023, 15(11), 8533; https://doi.org/10.3390/su15118533

Submission received: 26 March 2023 / Revised: 5 May 2023 / Accepted: 6 May 2023 / Published: 24 May 2023

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Abstract

Traffic collisions are one of the leading causes of traffic congestion. In the case of urban intersections, traffic accidents can even result in widespread traffic paralysis. To solve this problem, we developed an autoencoder-based model for identifying intersection entrance accidents by analyzing the characteristics of traffic volume. The model uses the standard deviation of the intersection entrance lanes’ traffic volume as an input parameter and identifies intersection entrance accidents by comparing predicted data to actual measured data. In this paper, the detection rate and average detection time are chosen to evaluate the effectiveness of algorithms. The detection rate of the autoencoder model reaches 94.33%, 95.47%, and 81.64% during the morning peak, evening peak, and daylight off-peak periods, respectively. Compared to the support vector machine and the random forest, autoencoder has better performance. It is evident that the research presented in this paper can effectively enhance the detection effect and has a shorter detection time of intersection entrance accidents.

Keywords: intersection entrance accidents; influence mechanism; autoencoder; accident identification

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MDPI and ACS Style

Du, Y.; Sun, F.; Jiao, F.; Liu, B.; Wang, X.; Zhao, P. The Identification of Intersection Entrance Accidents Based on Autoencoder. Sustainability 2023, 15, 8533. https://doi.org/10.3390/su15118533

AMA Style

Du Y, Sun F, Jiao F, Liu B, Wang X, Zhao P. The Identification of Intersection Entrance Accidents Based on Autoencoder. Sustainability. 2023; 15(11):8533. https://doi.org/10.3390/su15118533

Chicago/Turabian Style

Du, Yingcui, Feng Sun, Fangtong Jiao, Benxing Liu, Xiaoqing Wang, and Pengsheng Zhao. 2023. "The Identification of Intersection Entrance Accidents Based on Autoencoder" Sustainability 15, no. 11: 8533. https://doi.org/10.3390/su15118533

APA Style

Du, Y., Sun, F., Jiao, F., Liu, B., Wang, X., & Zhao, P. (2023). The Identification of Intersection Entrance Accidents Based on Autoencoder. Sustainability, 15(11), 8533. https://doi.org/10.3390/su15118533

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The Identification of Intersection Entrance Accidents Based on Autoencoder

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