Identification of Black Spots on Highway with Kernel Density Estimation Method

Sandhu, Har Amrit Singh; Singh, Gyanendra; Sisodia, Mahendra Singh; Chauhan, Rajeev

doi:10.1007/s12524-015-0500-2

Cited by 33 publications

(15 citation statements)

References 6 publications

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“…SI was integrated in KDE method to investigate accident hotspots on some highways in India. However, temporal-spatial integration was not mentioned (Sandhu et al 2016), despite of using SI in the hotspot study (Iyanda 2019).…”

Section: Introductionmentioning

confidence: 99%

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

Liu

Lin

2019

Geo-spatial Information Science

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This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi's weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

Liu

Lin

2019

Geo-spatial Information Science

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“…Based on the calculated weights for various types of road sections, the safety entropy values of the 46 road sections were calculated using (7) and (8). For example, the eight sections of road A have safety entropy values of 0.0436, 0.0278, 0.0318, 0.0385, 0.0439, 0.0358, 0.0277, and 0.0204.…”

Section: Calculation Of the Rtse Values Of The Road Sectionsmentioning

confidence: 99%

“…Additionally, the silhouette coefficients for various numbers of clusters were calculated. e silhouette coefficients for k of 2, 3, and 4 were found to be 0.44, 0.37, (1) int r ⟵ k-means number of clusters (2) For int t � 1 to r − 1 (3) int a t ⟵ Safety entropy value of the center of the t th cluster (4) int b t ⟵ Safety entropy value of the center of the (t + 1)th cluster (5) int s t ⟵ Sum of the data in the tth and (t + 1) th clusters (6) int f � 1 (7) For float e tf � a t to b t (8) int c tf ⟵ Volume of data in the tth cluster that is misclassified (9) int d tf ⟵ Volume of data in the (t + 1)th cluster that is misclassified (10) g tf � 1 − ((c tf + d tf )/s t )//Calculation of accuracy (11) e tf � etf + 0.01 (12) B t (f, 1: 2) � [e tf , g tf ]// e threshold and accuracy are stored in the matrix B t (13) f � f + 1 (14) End for (15) C t ⟵ Generation of the threshold corresponding to the highest accuracy (16) End for (17) C � [C 1 , C 2 , . .…”

Section: Determination Of the Number Of Risk Levelsmentioning

confidence: 99%

“…Mohan et al and Wang et al studied urban traffic safety evaluation methods based on accident rates [5,6]. Sandhu et al evaluated road traffic accident black spots using the kernel density estimation method based on road traffic accident data [7]. Dang et al established a regional road traffic safety evaluation index system by multiple correlation analysis of traffic accident data [8]; similarly, these researchers evaluated urban road traffic safety based on accident rates.…”

Section: Introductionmentioning

confidence: 99%

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Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data

Cai

Lei

Peng

et al. 2020

Journal of Advanced Transportation

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Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.

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“…KDE is the strongest in terms of statistical performance -on par with EB -and has been researched extensively [12]. KDE is particularly useful for HSID when combined with other methods like repeatability analysis [13], statistical analysis [14], and K-means clustering [15].…”

Section: A Hotspot Identification and Analysismentioning

confidence: 99%

The Costs of Traffic Accident Hotspots

Gahr

Caves

Wen

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Despite efforts to reduce them, traffic accidents continue to increase and bypass reduction targets. The costs of traffic accidents are enormous, killing 1.35 million people every year and costing 3% of most countries' GDP. Recent research aims to target interventions at high-accident-density locations, called accident hotspots. New methods and technologies can systematically identify hotspots, but it remains unclear whether hotspots contribute to accident costs as well as volume. This paper investigates the monetary and human costs of accident hotspots. We analyze a dataset of all accidents from 2011-2017 in Switzerland. We identify hotspots, then analyze their contributions to traffic accident costs. We find that hotspot accidents are not different in monetary costliness or injury rates from non-hotspot accidents, so hotspots drive costs along with accident volume. However, hotspot accidents are less fatal, so hotspot targeting might not be best for fatalities. If hotspots are reduced to normal road conditions, total monetary costs can be reduced by up to 5% per year as a theoretical upper bound. Targeting the top 10% most frequent, costly, injurious, or deadly hotspots yeilds different results for different cost types, with accident number and monetary cost targets creating the highest reductions overall.

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Identification of Black Spots on Highway with Kernel Density Estimation Method

Cited by 33 publications

References 6 publications

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data

The Costs of Traffic Accident Hotspots

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