Comparison Different Black Spot Identification Methods

Ghadi, Maen; Török, Árpád

doi:10.1016/j.trpro.2017.12.104

Cited by 33 publications

(17 citation statements)

References 8 publications

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“…Methods to identify accident concentration locations are a topic attracted an amount scientists [1,7,8,9]. Such research studies created a background for methodologies used by road authorities in different countries to treat dangerous road sections of the network.…”

Section: Description Of the Methods Used For Bsm And Nsm In The Baltic Statesmentioning

confidence: 99%

Comparative analysis of methods to identify road sections with high accidents risk: a case study of E67 Estonia–Latvia–Lithuania corridor

Pashkevich¹,

Pashkevich

2021

MATEC Web Conf.

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E67 road is a strategically important part of a North Sea – Baltic Core Network Corridor, connecting the three Baltic States with Finland, on the one hand, and with North Eastern Poland, on the other. So-called Via Baltica corridor services more than 30 000 vehicles per day being one of the major arteries for transit and heavy good vehicles transport in the region. Annually around 8 000 road accidents with casualties occur in the three Baltic States with more than 500 fatalities a year. Relatively high road safety risk exposure requires more efficient management of infrastructure safety issues. The three Baltic States use either black spot management (BSM) or network safety management (NSM) or a combination of these two approaches to treat dangerous road sections of the network. In this article three methodologies used in the Baltic countries for dangerous road sections and spots identification were described. Quantitative analysis of dangerous sections/spots identified by the three methodologies was performed for the whole Via Baltica corridor to reveal the differences between the methods used.

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Section: Description Of the Methods Used For Bsm And Nsm In The Baltic Statesmentioning

confidence: 99%

Comparative analysis of methods to identify road sections with high accidents risk: a case study of E67 Estonia–Latvia–Lithuania corridor

Pashkevich¹,

Pashkevich

2021

MATEC Web Conf.

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“…• Average Annual Daily Traffic (AADT): AADT is one important factor in predicting accident frequencies (Hiselius, 2004). • Speed limit: It is also an important factor in accident risk (Ghadi and Török, 2017). • Percent of trucks and small vehicles.…”

Section: Ranking Road Segments By Severitymentioning

confidence: 99%

“…This is not surprising as driver speed and speed limits have been recognized as one of the main contributing factors of road accidents. Speed limits can affect accident likelihood, severity, and density per road segment (Ghadi and Török, 2017). This rule is almost dominated by accidents of the second hazardous road segments (i.e.…”

Section: Extracting Decision Rule From Decision Treementioning

confidence: 99%

Evaluation of the Impact of Spatial and Environmental Accident Factors on Severity Patterns of Road Segments

Ghadi

Török

2020

Period. Polytech. Transp. Eng.

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Several studies have examined a wide range of accident risk factors affecting road safety. The purpose of this study is to examine the main traffic accident factors that affect the severity of road segments. The practical objective of the article is to assist specialists in identifying risk patterns both from a spatial and casualty point of view. To achieve the desired goals, accidents of a road network have been analyzed through three major steps; segmentation, black spot identification, and decision analysis. A new spatial clustering methodology has been used to divide accidents into smaller groups (or clusters) based on their spatial aggregations. The spatial characteristics are argued to be an important factor, in revealing the heterogeneity between accident data. Then, the empirical Bayesian has been applied to rank the resulted segments by severity level. During this step, the technique of decision rules has been applied to identify the main contributors to accidents in certain segments. The result shows that there is a significant relationship between the accident severity level and the traffic and geometrical characteristics (i.e. speed limits, average daily traffic, path shape) of road segments. The results also revealed that the closer the road to secure and non-hazardous road environmental conditions, the lower the risk level of the road segment.

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“…Several methods have been introduced for identifying hot zones [8][9][10]. Each study has advantages and drawbacks and is designed to address hotspots identification issues in a specific environment.…”

Section: Introductionmentioning

confidence: 99%

Traffic Accidents Detection using Geographic Information Systems (GIS)

Alkhadour¹,

Zraqou²,

Al-Helali³

et al. 2021

IJACSA

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The mission of reducing the number and severity of traffic accidents becomes the dominant target of road traffic safety management worldwide. The main objective of this work is to analyze traffic accidents in temporal and spatial frameworks in the capital city Amman and identify hotspot zones in the study area. Several statistical analyses are conducted using SQL to give insight into the temporal distribution of accidents and to identify the most revealing accidents based on several attributes such as the year of accidents, the severity of accidents, road type, and lighting environment which enables the authors to do further investigations on the more frequent accidents. GIS-based statistical and spatial analysis tools are utilized to examine the spatial pattern of accident distribution in the study area for three successive years, hotspots are identified for clusters of high concentrations. The Nearest Neighbor Index (NNI) is used to analyze the pattern of traffic accident distribution based on selective parameters. This was followed by identifying hotspot zones for regions that showed clustering using the optimal hotspot analysis tool. Experimental results showed clustering for all tested groups, and thus hotspots were detected for these accidents in the study area. The importance of this work is in providing a spatial understanding of accident distribution in the capital city of Amman which can help policymakers of road safety setting out efficient strategies for traffic safety management and find optimal solutions as required for factors causing such accidents.

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Comparison Different Black Spot Identification Methods

Cited by 33 publications

References 8 publications

Comparative analysis of methods to identify road sections with high accidents risk: a case study of E67 Estonia–Latvia–Lithuania corridor

Comparative analysis of methods to identify road sections with high accidents risk: a case study of E67 Estonia–Latvia–Lithuania corridor

Evaluation of the Impact of Spatial and Environmental Accident Factors on Severity Patterns of Road Segments

Traffic Accidents Detection using Geographic Information Systems (GIS)

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