Developing the crash modification model for urban street lighting

Saljoqi, Masoud; Behnood, Hamid Reza; Mirbaha, Babak

doi:10.1007/s41062-020-00440-6

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Therefore, identifying, studying, and evaluating the contributing factors affecting road safety is inevitable to increase the level of safety management. Several parameters affect road safety, and valuable studies have been conducted to investigate road safety, such as Driver Behavior [1][2][3][4][5][6][7], Age of Driver and Vehicle [8][9][10][11][12][13], Weather Conditions [14][15][16][17][18], Road Geometry [19][20][21][22][23], and Lighting Conditions [24][25][26][27][28][29]. Siliquini et al (2010) investigated the link between driving performance and the use of psychoactive drugs in teen drivers who were enrolled at common consuming locations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

View full text Add to dashboard Cite

show abstract

“…Therefore, identifying, studying, and evaluating the contributing factors affecting road safety is an inevitable thing to increase the level of safety management. Several parameters affect road safety and valuable studies have been conducted to investigate road safety, such as Driver Behavior [1][2][3][4][5][6], Age of Driver and Vehicle [7][8][9][10][11][12], Weather Conditions [13][14][15][16][17], Road Geometry [18][19][20][21][22], Lighting Conditions [23][24][25][26][27][28]. Zoe et al (2018) carried out an efficient analysis of the expansion trend on road safety researches between 2000 and 2018.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido¹,

Haghshenas²,

Haghshenas³

et al. 2021

Preprint

View full text Add to dashboard Cite

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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Safety and Economic Effects of Median Guardrails in No-Passing Zone Sections of a Two-Lane Two-Way Rural Highway

Mirbagheri,

Hosseinian,

Abdi Kordani

et al. 2024

Int J Civ Eng

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Developing the crash modification model for urban street lighting

Cited by 5 publications

References 20 publications

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Safety and Economic Effects of Median Guardrails in No-Passing Zone Sections of a Two-Lane Two-Way Rural Highway

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