Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis

Effati, Meysam; Rajabi, Mohammad; Hakimpour, Farshad; Shabani, Shahin

doi:10.1061/(asce)cp.1943-5487.0000432

Cited by 17 publications

(14 citation statements)

References 48 publications

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“…11,[14][15][16][17][18][19][20][21][22][23] Moreover, due to the importance of knowledge acquisition from large data set of crash records, data mining methods have become an essential component in many road safety studies. Numerous data mining 12,13,[24][25][26][27][28][29][30][31] related studies have been undertaken to analyze crash records locally and globally with frequently varying results depending on the study area and the methods used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…in road vicinities . Each technique is developed for a specific intercity road and needs to be redeveloped for other corridors of interest . In addition, they could not put into use the complex relations among crashes factors and crash severity for the proper prediction of accident‐prone vehicles …”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10] Each technique is developed for a specific intercity road and needs to be redeveloped for other corridors of interest. [10][11][12][13] In addition, they could not put into use the complex relations among crashes factors and crash severity for the proper prediction of accident-prone vehicles. 8,10 The size of spatial databases and the complexity involved in dealing with spatial attributes of crashes require the application of specialized data mining and geospatial techniques.…”

Section: Introductionmentioning

confidence: 99%

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A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

Effati

Sadeghi‐Niaraki

2015

WIREs Data Min & Knowl

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Innovative data mining and knowledge discovery approaches that take advantage of geospatial analysis can be useful for analysis of motor vehicle crashes on regional highway corridors. This study presents an Ontology-based Classification and Regression Tree (OCART) approach to induct crash rules on regional highway corridors by adding ontological reasoning to Classification and Regression Tree (CART) decision rules. It develops an ontology-driven geospatial framework to predict the motor vehicles crash severity through the proposed OCART approach. A system prototype has been developed and implemented on a regional highway corridor in order to illustrate and evaluate the proposed method. The results demonstrate the application of ontological reasoning and spatial computation significantly improves the performance of CART crash rules. The proposed method reveals new relationships among crash severities and contributing factors that have been kept implicit in CART decision rules.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

Effati

Sadeghi‐Niaraki

2015

WIREs Data Min & Knowl

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“…Additionally, SVM is a novel small sample learning method with a solid theoretical foundation, which has been used in many studies [21]. SVM is different from traditional algorithms [22,23].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

Chen

Zhang

Xing

et al. 2020

Journal of Advanced Transportation

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The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

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“…More recently, zero-inflated Poisson and zero-inflated negative binomial models were also employed to analyze accident frequencies to deal with the over-dispersion problem potentially caused by the extra zero in traffic accident data ( 9 , 10 ). A few studies on two-lane highways applied tree-based models to analyze accident rates and injury severity problems ( 11 – 13 ). However, in India, road crash data are not always well maintained or accessible, making reactive analysis such as statistical crash modeling and development of safety performance functions rather difficult.…”

mentioning

confidence: 99%

Safety Assessment of Two-Lane Highway using a Combined Proactive and Reactive Approach: Case Study from Indian National Highways

Chatterjee

Mitra

2019

Transportation Research Record

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In India, approximately 30% of road accident fatalities occur on two-lane rural roads. Thus research focused on identifying risk factors on this road type is of immense interest to most of the road agencies. Although these highways are more hazardous and account for more severe crashes, there is a lack of scientific safety assessment of such highways compared with multi-lane highways. Proactive approaches, such as road safety audit, have been widely adopted by the government of India to reduce crash frequency and severity on highways. However, an effective road safety management program should exercise an optimal balance between reactive and proactive strategies to identify potential hazards and treat already existing hazardous sites. Through a case study on two two-lane highways, several risk factors were identified using the principles of road safety audit, and were mapped with the available crash data analysis to develop a risk matrix. This risk matrix was found to be helpful in the selection of countermeasure design in a more scientific way, targeting the frequent crash types and severities expected to result at the high crash sites. Finally, it was observed that integrating the findings from reactive analysis with proactive safety management is more beneficial, since they are methodically proven with historical crash records and provide the knowledge of plausible safety hazards at sites where similar features exist. The proposed methodology could be adopted by road agencies in India and other developing countries for effective proactive safety planning.

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Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis

Cited by 17 publications

References 48 publications

A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

Safety Assessment of Two-Lane Highway using a Combined Proactive and Reactive Approach: Case Study from Indian National Highways

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