Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System

Abstract

With the rapid development of urban road traffic, there are a certain number of black spots in an urban road network. Therefore, it is important to create a method to effectively identify the urban road black spots in order to quickly and accurately ensure the safety of residents and maintain the sustainable development of a city. In this study, a GIS (geographic information system) and the Firefly Clustering Algorithm are combined. On the one hand, a GIS can accurately extract the distance between accident points through its spatial analysis function, overcoming the disadvantage of the accident data not usually including the specific location data. On the other hand, the Firefly Clustering Algorithm can be used to comprehensively extract the characteristics of accident points, which is particularly suitable for the identification of black spots. In order to verify the feasibility of the proposed method, this research compares the identification effect between the OD (origin–destination) cost distance calculated by GIS and the Euclidean distance. The results show that the Euclidean distance is smaller than the OD cost distance and that the accident search method based on the Euclidean distance can overestimate the number of black spots, especially for intersections. Therefore, the proposed method based on the Firefly Clustering Algorithm and GIS can not only contribute to identifying urban road black spots but also plays an auxiliary role in reducing urban road crashes and maintaining sustainable urban development.

Identification and Prioritization Techniques Applied to Urban Accident-Prone Locations: A Scoping Review

“…Fourteen grouping methods were found among the 42 studies evaluated (Table 3), highlighting the use of section split (SS) (Al-Omari et al, 2020;Greibe, 2003;Guerrero-Barbosa and Santiago-Palacio, 2016;Halim et al, 2018;Hayidso et al, 2019;Nguyen et al, 2016;Qu et al, 2019;Saha et al, 2020;Sugiyanto et al, 2017;Yan et al, 2019), kernel density estimation (KDE) (Hegyi et al, 2017;Le et al, 2020aLe et al, , 2020bPleerux, 2020;Shafabakhsh et al, 2017;Soltani and Askari, 2014;Xie and Yan, 2013), network kernel density estimation (NKDE) (Al-Aamri et al, 2021;Fan et al, 2018;Nie et al, 2015;Xie and Yan, 2008), spatial autocorrelation (SA) (Chance Scott et al, 2016;Steenberghen et al, 2011;Ulak et al, 2019), community census (CC) (Dong et al, 2016;Dumbaugh et al, 2010;Vaz et al, 2017), and cells (Cui and Xie, 2021;Debrabant et al, 2018;Geurts et al, 2005;Xiao et al, 2021). Less frequently used were beta-binomial screening (Park and Sahaji, 2013b), city limits , DBSCAN (Szénási and Jankó, 2017), the Firefly algorithm (Yuan et al, 2020), Gaussian mixture models (GMMs) (Mansourkhaki et al, 2017), nearest neighbor (NN) (Rahman et al, 2020)…”

“…Among the identified studies that used clusters to assess APLs, the techniques employed included weighted density analysis of RTAs, in order to identify locations with higher densities than expected (Szénási and Jankó, 2017), as well as expected frequency analysis (Mansourkhaki et al, 2017). Other approaches considered spatiotemporal variation in the distribution of accidents (Chance Scott et al, 2016;Le et al, 2020a;Soltani and Askari, 2014), the use of clusters to perform neighborhood analysis (Steenberghen et al, 2011;Xie and Yan, 2013), application of SPFs (Rahman et al, 2020), severity index measures (Hegyi et al, 2017;Ulak et al, 2019), identification of frequencies higher than the upper control limit (Pleerux, 2020;Shafabakhsh et al, 2017;Yuan et al, 2020), analysis of contributing factors (Fan et al, 2019), and potential improvement approaches such as cost savings (Le et al, 2020b).…”

“…The modified DBSCAN algorithm was applied using a sliding window technique to weight the APL severity and identify clusters (Szénási and Jankó, 2017). The Firefly algorithm was used with connectivity criteria to find clusters with densely connected points (Yuan et al, 2020). The GMM was used to identify clusters from probabilistic density functions and provide the probability of occurrence of RTAs at each location (Mansourkhaki et al, 2017).…”

“…The MTL method adopts thresholds such as the minimum number of occurrences in the period, values above the expected average, or predefined quantiles. The use of MTL was mainly identified in studies investigating sites with occurrences or severity indexes greater than a threshold value (Debrabant et al, 2018;Halim et al, 2018;Park and Sahaji, 2013;Yan et al, 2019;Yuan et al, 2020). MTL was also used in studies evaluating potential improvements, estimating the gains likely to be generated by implementing mitigation measures (Nguyen et al, 2016).…”

Traffic Accidents Detection using Geographic Information Systems (GIS)

“…A GIS-based system incorporated with the Firefly Clustering algorithm was presented in [24] to identify hot zones of accidents. spatial analysis tools existing in ARC-GIS were used to find distances between accident points, while characteristics of accidents were identified by applying a Firefly Clustering algorithm.…”