Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach

Driss, Miloud; Benabdeli, Khéloufi; Saint-Gérand, Thierry; Hamadouche, Mohamed

doi:10.1080/10106049.2014.883554

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…This is due to land limitations and topographic conditions that are not flat [8]. This opinion follows what has been expressed that inconsistent geometric design in mountainous areas with technical requirements will have a high accident risk [9].…”

Section: Introductionsupporting

confidence: 60%

“…Previous studies show evidence that various aspects need to be considered to realize road traffic safety. Improving safety performance needs to pay attention to the geometric aspects of the road [8], the existing spatial conditions of the terrain [9], and the behavior of road users [10]. This is very relevant to the importance of road safety because of the factors that cause accidents even though they are caused by various factors [11], but the geometric aspect is very important [12].…”

Section: Introductionmentioning

confidence: 99%

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Accident risk analysis of road geometric components using functional worthiness approach

Suraji

Mulyono

2022

EEJET

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The geometric design of the road should be based on the road design criteria and requirements contained in the design guidelines. However, the limitations of natural conditions and considerations during the design make the road design not ideal according to the guidelines. Deviations from the guidelines may indicate that the road is not standard and has potential accident risk. This study intends to analyze the risk of accidents from the geometric aspect of the road. The purpose of this study is to find out how the level of accident risk is due to the geometric conditions of the road. The approach used in this analysis is functional worthiness where each road component is compared with field data and design guidelines. The data collection method was carried out by measuring the geometric components of the road, which included horizontal and vertical alignments. The location of data collection was national roads in the Jambi Province area with a total road length of 1.095 km. For analysis purposes, the road data is divided into 82 segments. The analytical method used is multiple regression with various statistical parameters related to these results. The modeling results show that the regression coefficients for each variable horizontal alignment, vertical alignment, and combination of horizontal and vertical alignment are 0.248, 0.349, and 1.170, respectively. While the constant in this regression equation model is 3.366. The greatest value of the accident risk contribution is in the horizontal and vertical alignment variables with a coefficient of 1.170. The results of statistical tests show that the relationship between accident risk and the geometric aspect of the road has a strong linear relationship

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Section: Introductionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Accident risk analysis of road geometric components using functional worthiness approach

Suraji

Mulyono

2022

EEJET

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show abstract

“…The work carried out by Fuller et al [55] does generate a risk map and compares it with real data, but they only take into account four road safety factors and use a multi-criteria evaluation method that does not take into account the weighting of either experts or road users. The work of Driss et al [56] validates the generated map, but they did not obtain it using MCDM. In the work carried out by Hu et al [57], in addition to not using any MCDM, they only compared the generated map with field-based interviews and not with real accident data.…”

Section: Discussionmentioning

confidence: 99%

A top-down approach for a multi-scale identification of risk areas in infrastructures: particularization in a case study on road safety

Rúa

Comesaña-Cebral

Arias

et al. 2022

Eur. Transp. Res. Rev.

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Introduction Transport infrastructures have an important function in society and the development of a country. In Spain, the most used modes of traveler transport are road and rail, far ahead of other means of transport such as air or maritime transport. Both rail and road infrastructures can be affected by numerous hazards, endangering their performance and the safety of users. This study proposes a methodology with a multiscale top-down approach to identify the areas affected by fire, landslide, and safety in road and rail infrastructures in Galicia (Northwest Spain). Methodology The methodology is developed in three steps, coinciding with the three scales considered in this work: network-, system-, and object-level. In the first step, risk areas are identified and prioritized, resulting in the most critical safety risk in a motorway section. This area defines a study scenario composed of a location (A-55 motorway) and the associated risk (road safety). In the second step, the road safety factors within this scenario are selected, hierarchized, and weighted using a combination of Multi-Criteria Decision-Making methods including the Analytical Hierarchy Process and the Best–Worst Method. Finally, a risk map is generated based on the weighting of infrastructure-related safety factors and compared to real historical accident data for validation. The methodology is based on road and risk assessment standards and only information in the public domain is used. Results Results show that only 3 segments out of 153 were classified incorrectly, which supports a probability higher than 95% of agreement with real data (at 5% significance level). In a conclusion, the overall methodology exhibits a high potential for hazard prevention and road-safety enhancement.

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“…Because RTA causes significant financial damage to countries and leads to many people's death every year [6], its modeling has always been a hot topic. In general, the available traffic accident models are in three categories of severity modeling [7][8][9], risk modeling [10,11], and frequency modeling [12,13]. These models aids in the prediction of road safety conditions by explaining the influence of several elements on accident occurrence [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

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Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach

Cited by 14 publications

References 34 publications

Accident risk analysis of road geometric components using functional worthiness approach

Accident risk analysis of road geometric components using functional worthiness approach

A top-down approach for a multi-scale identification of risk areas in infrastructures: particularization in a case study on road safety

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

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