New methods to identify and rank high pedestrian crash zones: An illustration

Pulugurtha, Srinivas S.; Krishnakumar, Vanjeeswaran; Nambisan, Shashi

doi:10.1016/j.aap.2006.12.001

Cited by 124 publications

(67 citation statements)

References 5 publications

(5 reference statements)

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“…KDE is an example which has been used in road safety to study the spatial pattern of crash and identify the hotspots [8][9][10][11][12]. Similarly, there are other geostatisical methods such as clustering methods that evaluate relative risk based on their degree of association with its surroundings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The expected crash frequency could also be estimated using a geostatistical technique by considering the effects of unmeasured confounding variables through the concept of spatial autocorrelation between the crash events over a geographical space [9][10][11][12][13]. KDE is an example which has been used in road safety to study the spatial pattern of crash and identify the hotspots [8][9][10][11][12].…”

Section: Literature Reviewmentioning

confidence: 99%

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Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

2015

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This paper presents a study aimed at comparing the outcome of two geostatistical-based approaches, namely kernel density estimation (KDE) and kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification is an integral component of any comprehensive road safety management programs. A case study was conducted with historical crash data collected between 2003 and 2007 in the Hennepin County of Minnesota, U.S. The two methods were evaluated on the basis of a prediction accuracy index (PAI) and a comparison in hotspot ranking. It was found that, based on the PAI measure, the kriging method outperformed the KDE method in its ability to detect hotspots, for all four tested groups of crash data with different times of day. Furthermore, the lists of hotspots identified by the two methods were found to be moderately different, indicating the importance of selecting the right geostatistical method for hotspot identification. Notwithstanding the fact that the comparison study presented herein is limited to one case study, the findings have shown the promising perspective of the kriging technique for road safety analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

2015

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“…이러한 사업을 진행할 때, 어떤 지점에 안전 대책을 우 선적으로 수립하여야 교통안전을 크게 개선할 수 있는지 파 악하는 것은 매우 중요하다 (Khan et al, 2008). 많은 연구들이 교통사고 핫스팟 선정에 KDE를 사용하였 으며 (Anderson, 2009;Flahaut et al, 2003;Plug et al, 2011;Prasannakumar et al, 2011;Truong and Somenahalli, 2011;Vemulapalli, 2015), 일부 연구들은 KDE를 시행하여 보행자 사고 핫스팟을 선정하였다 (Blazquez and Celis, 2013;Jang et al, 2013;Loo et al, 2011;Pulugurtha et al, 2007;Rankavat and Tiwari, 2013 일부 연구들은 Getis-ord Gi*를 시행하여 교통사고 핫스팟 을 선정하였다 (Khan et al, 2008;Kingham et al, 2011;Kuo et al, 2011;Manepalli et al, 2011;Vemulapalli, 2015). 특히, Getis-ord Gi*를 사용한 기존 연구들은 Getis-ord Gi*로 …”

Section: 서 론unclassified

A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul

양종현¹,

김정옥²,

유기윤³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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To establish objective criteria for high pedestrian accident zones, we combined Getis-ord Gi* and Kernel Density Estimation to select high pedestrian accident zones for 54,208 pedestrian accidents in Seoul from 2009 to 2013. By applying Getis-ord Gi* and considering spatial patterns where pedestrian accident hot spots were clustered, this study identified high pedestrian accident zones. The research examined the microscopic distribution of accidents in high pedestrian accident zones, identified the critical hot spots through Kernel Density Estimation, and analyzed the inner distribution of hot spots by identifying the areas with high density levels.Keywords: Pedestrian Accident, Hotspot, Getis-ord Gi*, Kernel Density Estimation This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Furthermore, the most typical methods based on discrete events may be the nearest neighbor distance method, Ripley's K function methods [26], Kernel Density Estimation (KDE) methods [27,28] and others. Traditionally, the KDE methods have been widely used in point-pattern analyses for discrete events, especially in TC analyses [14,29,30]. Although no single technique has emerged as the "best" for detecting and predicting TC clusters, recent research suggests that KDE outperforms other approaches due to its simplicity and easy implementation [31].…”

Section: Introductionmentioning

confidence: 99%

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

Nie

Wang

et al. 2015

Sustainability

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Abstract:Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration. OPEN ACCESSSustainability 2015, 7 2663

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New methods to identify and rank high pedestrian crash zones: An illustration

Cited by 124 publications

References 5 publications

Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

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