GIS-based method for detecting high-crash-risk road segments using network kernel density estimation

Mohaymany, Afshin Shariat; Shahri, Matin; Mirbagheri, Babak

doi:10.1080/10095020.2013.766396

Cited by 98 publications

(52 citation statements)

References 27 publications

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“…Comparisons between ED and RND were also presented while analyzing accident hotspots for the whole population (i.e. all driving ages) by Mohaymany et al (2013) and Dai (2012). A recent paper (Lamb et al 2015) studied the network Kfunctions using network distance measures for the following types of networks: the actual roadway network, topologically correct network, and directionally constrained network.…”

Section: Spatial Analysismentioning

confidence: 98%

GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida

Vemulapalli

Ulak

Özgüven

et al. 2016

Appl. Spatial Analysis

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Roadway accidents claim more than 30,000 lives each year in the United States, and they continue adversely affecting people's well-being. This problem becomes even more challenging when aging populations are considered due to their vulnerability to accidents. This is especially a major concern in Florida since the accident risk is increasing proportionally to the population growth of aging Floridians. This study investigates the spatial and temporal patterns of aging people-involved accidents using geographical information systems (GIS)-based methods via a case study of three urban counties in Florida, selected based on their high aging-involved accident rates. A series of spatial analytic methods are utilized to explore accident patterns, including a network distance-based kernel density estimation method, which provides an unbiased distribution of the accidents over the local roadways. An accident density ratio measure is also developed in order to understand how accidents involving aging people occur at different locations than those of the general population. Results indicate that high risk locations for aging-involved accidents show different spatial and temporal patterns than those for other age groups. Investigating these distinct patterns at a high spatio-temporal scale can lead to better aging-focused transportation plans and policies.

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Section: Spatial Analysismentioning

confidence: 98%

GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida

Vemulapalli

Ulak

Özgüven

et al. 2016

Appl. Spatial Analysis

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“…Also, the KDE method may outperform the empirical Bayesian method in the identification of hazardous road segments when only the location of the crash can be used for the analysis [32]. However, although the KDE has shown acceptable properties using density values, its homogeneous 2D assumption for events distributed in 1.5D space, such as TC on a road network, seems to be irrelevant [33][34][35][36][37][38]. To overcome this limitation, Okabe proposed the idea of the spatial analysis based on a network, Network-Constrained Kernel Density Estimation (NKDE), which can overcome the shortcomings of the KDE method and reduce the deviation of its results [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this limitation, Okabe proposed the idea of the spatial analysis based on a network, Network-Constrained Kernel Density Estimation (NKDE), which can overcome the shortcomings of the KDE method and reduce the deviation of its results [39][40][41]. Furthermore, research has demonstrated the validity of NKDE to analyze network-based phenomena, such as TC [35,[42][43][44][45][46][47].…”

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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“…One of the main flaws is that the density area of not only in the networks but also of the other areas are detected and this leads to skewed results. For this reason, network kernel density estimation (NKDE) was suggested [6] and it has been used for various applications such as detection of the likelihood of a hot spot in vehicle incidents [7]. Not only NKDE, but also network spatial and temporal analysis of crime (NT-STAC) and network spatial scan statistics (NT-SaTScan) were introduced to detect crime occurrences (robbery, burglary, drug deals/use, etc.)…”

Section: Related Workmentioning

confidence: 99%

Detecting Themed Streets Using a Location Based Service Application

Ji¹,

Lee

et al. 2016

IJGI

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Abstract:Various themed streets have recently been developed by local governments in order to stimulate local economies and to establish the identity of the corresponding places. However, the motivations behind the development of some of these themed street projects has been based on profit, without full considerations of people's perceptions of their local areas, resulting in marginal effects on the local economies concerned. In response to this issue, this study proposed a themed street clustering method to detect the themed streets of a specific region, focusing on the commercial themed street, which is more prevalent than other types of themed streets using location based service data. This study especially uses "the street segment" as a basic unit for analysis. The Sillim and Gangnam areas of Seoul, South Korea were chosen for the evaluation of the adequacy of the proposed method. By comparing trade areas that were sourced from a market analysis report by a reliable agent with the themed streets detected in this study, the experiment results showed high proficiency of the proposed method.

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GIS-based method for detecting high-crash-risk road segments using network kernel density estimation

Cited by 98 publications

References 27 publications

GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida

GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida

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

Detecting Themed Streets Using a Location Based Service Application

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