Comparative analysis of the spatial analysis methods for hotspot identification

Yu, Hao; Liu, Pan; Chen, Jun; Wang, Hao

doi:10.1016/j.aap.2014.01.017

Cited by 118 publications

(62 citation statements)

References 18 publications

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“…Several methods have been developed to identify hazardous locations based on crash histories. They include the crash frequency method [12], the crash rate method [13][14][15], the equivalent property damage only method (EPDO) [16], the rate-quality control method [17], and the empirical Bayes (EB) method [18][19][20][21][22][23][24].…”

Section: Identification Of Hazardous Locationsmentioning

confidence: 99%

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

Chung

Won

2018

Sustainability

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Traditionally, traffic safety improvement programs (TSIPs) have been based on the number of crashes at a specific location or their severity. However, the crash datasets used for such programs are obtained from the police and include two limitations: not all crashes are collected by the police (most minor and near-miss crashes are not reported), and the traditional process uses crash data recorded for the past two or three years (meaning most data inevitably include a time lag). To overcome these limitations, this study proposes a new approach for a TSIP based on citizen participation through an online survey that is broadcasted through social media. The method uses the public as sensors of hazardous road information, which means that information can be collected on individual experiences of minor crashes and latent risk factors, such as near misses and traffic conflicts. To demonstrate this approach, a case study was carried out in a small district in the city of Goyang, Korea, which has one of the highest usage rates of social media technologies. The proposed method and a traditional method were both assessed.

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Section: Identification Of Hazardous Locationsmentioning

confidence: 99%

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

Chung

Won

2018

Sustainability

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“…First, a kernel density analysis was applied in order to identify areas with a higher concentration of point events (for example, areas with high potential for nature photography, criterion B15). In fact, a kernel density estimator (KDE) function transforms a sample of georeferenced observations (point events in a 2D space) into a continuous surface, indicating the intensity of individual observations over the 2D space [48]. A symmetric surface (called kernel function K) is placed at the center of each spatial unit(s) and distances between the center point and the locations of point events are evaluated in order to compute the point density at this location.…”

Section: Standardization Of Evaluation Criteriamentioning

confidence: 99%

Using a VGI and GIS-Based Multicriteria Approach for Assessing the Potential of Rural Tourism in Extremadura (Spain)

et al. 2016

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Extremadura (Spain), being one of the less-developed EU regions, is also eligible for European funds, including those related with the development of tourism activities in rural areas. To assess the spatial distribution of rural tourism potential (RTP), a methodological approach was followed by combining the synergetic use of geographic information systems (GIS) and the analytical hierarchical process (AHP). For the evaluation model, six factors were examined and hierarchized by a panel of eighteen experts: tourism accommodation offer, activities in natural areas, gastronomic offer, cultural activities, bath offer in natural environments, and activities in active tourism. These factors were then decomposed into thirty-two evaluation criteria and the relative importance of their weights was estimated using pairwise judgments. Using publicly available data and volunteered geographic information (VGI) platforms, the related spatial and non-spatial data were collected, collated, and standardized, employing appropriate tools developed with open-source GIS software. The results, represented by a series of intensity maps, indicate the RTP scores, highlight the strengths and weaknesses in each county, and could serve as a planning tool for public and/or private investments.

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“…A spatial Bayesian modeling approach was proposed by [28] to predict VRU accident risks for a road network and to identify how road infrastructures influence VRU safety in Brussels, Belgium. An approach was introduced by [29] for the identification of hazardous accident zones that compares spatial and non-spatial methods. Overall, the study concludes that spatial analysis methods outperform non-spatial approaches, because they do not require the segmentation of highways.…”

Section: Traffic Accident Researchmentioning

confidence: 99%

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Machado

Giannotti

Neto

et al. 2015

IJGI

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Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies,859 geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in São Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in São Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data.

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Comparative analysis of the spatial analysis methods for hotspot identification

Cited by 118 publications

References 18 publications

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

Using a VGI and GIS-Based Multicriteria Approach for Assessing the Potential of Rural Tourism in Extremadura (Spain)

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

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