Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach

Xie, Zhixiao; Yan, Jun Juh

doi:10.1016/j.jtrangeo.2013.05.009

Cited by 214 publications

(118 citation statements)

References 20 publications

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“…We used 200 m due to more obvious hot segments compared with other search widths from 100 m, 500 m and 1000 m. The lixel is like a resolution in a raster and the smaller lixel is, the higher the precision. Hence, the length of lixel in NKDE was set at 10 m and 40 m, which were also identified by Xie [47]. In addition, it may be shown that a Gaussian kernel is generally robust and a usual choice for KDE.…”

Section: Results Of the Kde And Nkde For Traffic Crash Eventsmentioning

confidence: 99%

“…When r decreases, the surface of the density becomes uneven, enhancing the cost of the calculation. Besides, it demonstrates that the effect of the choice of kernel function is less than the effect of the choice of the search width [47,48]. There are many kernel density functions, such as the Gaussian, Quartic, Conic, Negative exponential and Epanechnikov [51].…”

Section: Network Kernel Density Estimationmentioning

confidence: 95%

“…Although both local Moran's I and Getis-Ord Gi* are able to reveal hot spots, Getis-Ord Gi* is able to differentiate autocorrelation due to the spatial associations of high-high values or low-low values. Moreover, as Xie pointed, different methods of local statistics measurements should be tested when the KDE and LISA are integrated for cluster detection [47].…”

Section: Network Kernel Density Estimationmentioning

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%

See 3 more Smart Citations

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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Section: Results Of the Kde And Nkde For Traffic Crash Eventsmentioning

confidence: 99%

Section: Network Kernel Density Estimationmentioning

confidence: 95%

Section: Network Kernel Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Using a spatial methodology by the K-function has already been well received in the studies on traffic accidents (Yamada and Thill 2004;Xie and Yan 2013), where cluster patterns were detected. Kaygisiz et al (2015) mapped the focus of accidents using network-based kernel density estimation, K-function, and the nearest neighbor distance methods.…”

Section: Introductionmentioning

confidence: 99%

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

Lindner

Pitombo

2017

J geovis spat anal

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Conventional analysis of transportation demand is usually carried out using socioeconomic, travel, and land use attributes. Despite the effectiveness on travel demand forecasting, it is important to recognize that alternative approaches have been developed in recent years. Traditional methods, besides considering different explanatory variables, are appropriate to make estimates exclusively on previously surveyed households. On the other hand, recent studies have addressed spatial statistical concerns in the field of travel demand forecasting. The aim of this paper is to spatially estimate motorized travel mode choice probabilities in a continuous map using an Origin-Destination Survey database, conducted in the São Paulo Metropolitan Area in Brazil in 2007. Values were estimated in both sampled and non-sampled coordinates. This paper proposes a conjoint approach that combines the traditional procedure of travel demand forecasting (multiple logistic regression) with a spatial statistical method (ordinary kriging). A comparison is made with the one-step spatial method-indicator kriging (IK). Conjoint studies of spatial statistics and traditional methods are thriving in transportation analysis, giving rise to a travel mode choice surface in a confirmatory way. It is concluded that the proposed method can be used for future predictions of travel mode choices, unlike IK.

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Spatial Statistics along Networks

Okabe

2018

Wiley StatsRef: Statistics Reference Online

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Spatial statistics along networks is a branch of spatial statistics. Traditional spatial statistics deals with events occurring on a plane, referred to as planar spatial statistics . By contrast, spatial statistics along networks, referred to in parallel as network spatial statistics , deals with events occurring along networks. The events occurring “along” a network are of two types: those directly occurring on a network, for example, traffic accidents, and those occurring alongside a network, for example, stores facing a street. Networks may potentially include corridors, roads, railways, gas and oil pipelines, sewer systems, rivers, canals, electric wires, neurons, the Internet, social networks (e.g., SNS), and so on. Network spatial statistics is applied to various studies, for instance, pedestrian, car, and bicycle accidents, roadkill, street crimes, contamination of rivers, floods, facility locations, tree falls on a railway, etc. This article first describes the most basic hypothesis used in network spatial statistics, called the completely spatial randomness ( CSR ) hypothesis. Next, the article introduces nine methods used for network spatial statistics, that is, Voronoi diagrams, nearest‐neighbor distance method, K function method, spatial autocorrelation, point cluster analysis, kernel density estimation, spatial interpolation, and software for network spatial statistics.

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Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach

Cited by 214 publications

References 20 publications

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

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

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

Spatial Statistics along Networks

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