Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development

Shahin Shabani; Jalal Ayoubinejad; Nassir Baradaran Rahmanian

doi:10.7250/bjrbe.2024-19.629

Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development

Authors

Shahin Shabani Department of Civil Engineering, Payame Noor University, Tehran, Iran https://orcid.org/0000-0001-9131-0597
Jalal Ayoubinejad Department of Civil Engineering, Payame Noor University, Tehran, Iran https://orcid.org/0000-0001-9969-0918
Nassir Baradaran Rahmanian Department of Civil Engineering, Payame Noor University, Tehran, Iran https://orcid.org/0009-0001-1060-4487

DOI:

https://doi.org/10.7250/bjrbe.2024-19.629

Keywords:

black spots, density-based spatial clustering, network screening, optimization, sliding window, safety performance function

Abstract

The sliding window method is a road network screening approach commonly used for identifying black spots. Previous studies have indicated that the selection of window length significantly impacts the black spot identification process. This research proposes a new method that optimizes the sliding window framework by examining its characteristics. The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. The case study yielded noteworthy results, indicating that the utilization of the sliding window with optimal lengths led to the identification of 122 high-risk black spot-candidates. These points exhibit a higher crash density, effective length, and greater value in quantitative evaluation tests compared to the results obtained using windows with common fixed lengths.

References

Al-Omari, M., Abdel-Aty, M., & Cai, Q. (2021). Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system. Journal of Safety Research, 79, 1–13. https://doi.org/10.1016/j.jsr.2021.08.004

Baig, S., Iqbal, W., Berral, J.L., & Carrera, D. (2020). Adaptive sliding windows for improved estimation of data center resource utilization. Future Generation Computer Systems, 104, 212–224. https://doi.org/10.1016/j.future.2019.10.026

Bonera, M., Barabino, B., & Maternini, G. (2022). A straightforward framework for road network screening to Lombardy region. Sustainability, 14(19), Article 12424. https://doi.org/10.3390/su141912424

Cafiso, S., D’Agostino, C., & Persaud, B. (2018). Investigating the influence of segmentation in estimating safety performance functions for roadway sections. Journal of Traffic and Transportation Engineering, 5(2), 129–136. https://doi.org/10.1016/j.jtte.2017.10.001

Deng, D. (2020). DBSCAN clustering algorithm based on density. 7th International Forum on Electrical Engineering and Automation (IFEEA), Hefei, China, 949–953. https://doi.org/10.1109/IFEEA51475.2020.00199

El Bahi, H., & Zatni, A. (2018). Document text detection in video frames acquired by a smartphone based on line segment detector and DBSCAN clustering. Journal of Engineering Science and Technology, 13(2). https://www.researchgate.net/publication/322941007

Elvik, R. (2007). State-of-the-art approaches to road accident black spot management and safety analysis of road networks. European Commission, National Research Council of Norway, and Institute of Transport Economics. https://www.toi.no/getfile.php/139022-1210254330/Publikasjoner/TØI%20rapporter/2007/883-2007/883-2007-nett.pdf

Elvik, R. (2008a). Comparative analysis of techniques for identifying hazardous road locations. Transportation Research Record: Journal of the Transportation Research Board, 2083(1), 72–75. https://doi.org/10.3141/2083-08

Elvik, R. (2008b). A survey of operational definitions of hazardous road locations in some European countries. Accident Analysis & Prevention, 40(6), 1830–1835. https://doi.org/10.1016/j.aap.2008.08.001

Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–231. https://file.biolab.si/papers/1996-DBSCAN-KDD.pdf

Farid, A., Abdel-Aty, M., & Lee, J. (2018). Transferring and calibrating safety performance functions among multiple states. Accident Analysis & Prevention, 117, 276–287. https://doi.org/10.1016/j.aap.2018.04.024

Farid, A., Abdel-Aty, M. & Lee, J. (2019). Comparative analysis of multiple techniques for developing and transferring safety performance functions. Accident Analysis & Prevention, 122, 85–98. https://doi.org/10.1016/j.aap.2018.09.024

Fisher, R.A. (1992). Statistical methods for research workers. In S. Kotz, & N.L. Johnson (Eds.), Breakthroughs in Statistics. Springer Series in Statistics. Springer-Verlag, New York, Inc. https://doi.org/10.1007/978-1-4612-4380-9_6

Ghadi, M., & Török, Á. (2019a). A comparative analysis of black spot identification methods and road accident segmentation methods. Accident Analysis & Prevention, 128, 1–7. https://doi.org/10.1016/j.aap.2019.03.002

Ghadi, M., & Török, Á. (2019b). Comparison of different road segmentation methods. Promet – Traffic and Transportation, 31(2), 163–172. https://doi.org/10.7307/ptt.v31i2.2937

Ghadi, M. (2020). Methods of segmenting and analyzing of road accident data [Doctoral dissertation, Budapest University of Technology and Economics]. https://repozitorium.omikk.bme.hu/server/api/core/bitstreams/ca41b13b-6b0a-4ede-a9fc-73a1b1dbdb19/content

Green, E. R. (2018). Segmentation strategies for road safety analysis [Doctoral dissertations, University of Kentucky]. https://uknowledge.uky.Edu/ce_etds/62

HSM. (2010). Highway safety manual (1st ed.). American Association of State Highway and Transportation Officials, Washington, DC.

Kolody, K., Perez-Bravo, D., Zhao, J., & Neuman, T.R. (2022). Highway safety manual user guide. Transportation Research Board (NCHRP Project 17-50). https://doi.org/10.17226/26552

Kwon, O., Park, M., Yeo, H., & Chung, K. (2013). Evaluating the performance of network screening methods for detecting high collision concentration locations on highways. Accident Analysis & Prevention, 51, 141–149. https://doi.org/10.1016/j.aap.2012.10.019

Lee, J., Chung, K., Papakonstantinou, I., Kang, S., & Kim, D. (2020). An optimal network screening method of hotspot identification for highway crashes with dynamic site length. Accident Analysis & Prevention, 135, Article 105358. https://doi.org/10.1016/j.aap.2019.105358

Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. https://doi.org/10.1016/j.tra.2010.02.001

Medury, A., & Grembek, O. (2016). Dynamic programming-based hot spot identification approach for pedestrian crashes. Accident Analysis & Prevention, 93, 198–206. https://doi.org/10.1016/j.aap.2016.04.037

Srinivasan, R., & Bauer, K. (2013). Safety performance function development guide: developing jurisdiction specific SPFs. The University of North Carolina Highway Safety Research Center (Report FHWA-SA-14-005). https://safety.fhwa.dot.gov/rsdp/downloads/spf_development_guide_final.pdf

Szénási, S. (2016). Variable sized planar sliding window technique for searching accident hot spots. 16th International Multidisciplinary Scientific GeoConference SGEM2016, Bulgaria. https://www.researchgate.net/publication/312395090_VARIABLE_SIZED_PLANAR_SLIDING_WINDOW_TECHNIQUE_FOR_SEARCHING_ACCIDENT_HOT_SPOTS

Veeramisti, N., Paz, A., & Baker, J. (2020). A framework for corridor-level traffic safety network screening and its implementation using Business Intelligence. Safety Science, 121, 100–110. https://doi.org/10.1016/j.ssci.2019.08.042

Wali, B., Khattak, A., Waters, J., Chimba, D., & Li, X. (2018). Development of safety performance functions: Incorporating unobserved heterogeneityand functional form analysis. Transportation Research Record: Journal of the Transportation Research Board, 2672(30), 9–20. https://doi.org/10.1177/0361198118767409

Zhang, D., Peijie, W., & Xianghai, M. (2018). The optimal sliding window length and increment length for identifying hazardous road segments. Transportation Research Board 97th Annual Meeting, Washington, DC, USA.