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Road accidents have a relevant impact in terms of economic and social costs. As a consequence, many research studies have focused on identifying the key factors affecting accident severity. Traditionally, these factors can be included in the infrastructural, human and vehicle groups. Among these, human factors have a relevant impact on accident sev...
Similar publications
This study intended to investigate the injury severity of animal-vehicle crashes (AVCs) and identify the contributing factors from the perspective of animals. To achieve this objective, Bayesian random parameter probit regression model with endogenous variable was proposed with a case study application on two places in Inner Mongolia, China. First,...
Citations
... Additionally, other authors pointed out driver's behavior characteristics including cellphone usage while driving, risky driving behavior, especially for young drivers, and overtightening as the major causes of road accidents [7][8][9]. And, Eboli et al. [10] grouped the determinants of accident severity into three categories: Road, external environments, and driver characteristics. To predict traffic injury severity, both statistical methods and machine learning models have been used. ...
... Therefore, for computing P (C =c ) the relative frequencies for every class are employed to get Eq. (8). ...
Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.
... In addition to seat belt use, previous works showed that other factors corresponding to the driving behaviors such as overtaking, errors and distraction can contribute to the severity of traffic accidents [8][9][10]. Kaplan and Prato [11] found that drivers beyond the age of 55, female drivers, age and risky driving are likely to increase the risk of fatality. ...
Background
About 1.35 million people died in traffic accidents around the world in 2018, make this type of accidents the 8th cause of death in the world. Particularly, in Spain, there were 204,596 traffic accidents during 2016 and 2017, out of which 349,810 drivers were injured. The objective of this study was to understand to what extent seat belt non-use and human factors contribute to drivers injury severity.
Methodology
The results are based on the information and 2016–17 data provided by the Spain national traffic department “Dirección General de Tráfico” (DGT). The discretization model and Bayesian Networks were developed based on important variables from the literature. These variables were classified as; human factor, demographic factor, conditioning factor and seat belt use.
Results
The results showed that failure to wear the seat belt by drivers are likely to increase the risk of fatal and sever injury significantly. Moreover, distraction and road type road can contribute to the accident severity.
... Most of the literature studies dealing with traffic accidents focused on accident severity in terms of human fatalities and injuries. It is well-known that the main cause of accidents and crashes is human error [5][6][7]. Over-speeding, rash driving, violation of rules, failure to understand signs, fatigue, and alcohol are examples of common human behaviors which result in traffic accidents. Various authors adopted different kinds of mathematical models for identifying the influence of the various factors on the severity of an accident. ...
Analyzing traffic accidents is very important due to their direct impact on the social environment. In the literature, many studies focus on the different aspects that influence traffic accidents, such as human, vehicle, road and environment risk factors. In this paper, we propose a methodology for testing the relationship between road, external environment, driver and vehicle characteristics, and certain circumstances that lead to the traffic crashes. Particularly, we elaborate on logistic regression models for evaluating how these different characteristics impact on crash severity, considering the combination of traffic circumstances that caused the crash. In each combination, a vehicle proceeded regularly, whereas the other vehicle did an incorrect maneuver (the vehicle proceeded: with distracted driving; without maintaining the safety distance; with speeding; by maneuvering to join the circulation flow; against the flow). The present work analyzes data related to road crashes which occurred in Italy during 2016 involving two vehicles. The results show that the variables significantly influencing crash severity are different depending on the combinations of circumstances that cause the crash.
... Human factors have a relevant impact on accident severity. For this reason, many authors also focus on driving behavior, attitude and experience of car drivers, especially in the last few years where using mobile while driving and speeding driving behavior have become usual behavior among people, particularly among young people (Machado-León et al., 2016;Cardamone et al., 2016;Cardamone et al., 2017;Choudhary and Velaga, 2017). ...
Nowadays, road safety is an issue particularly relevant because of the increasing occurrence of road accidents. For this reason, it is very important to analyze accident severity and the factors influencing it. This paper aims to investigate on the characteristics that can influence the severity of an accident by distinguishing between the various types of accident (e.g. front/side collision, rear-end collision). The included related factors were grouped in different categories referring to road, external environment, and driver. The proposed analysis has the specific objective to discover the possible differences between accidents severity expressed through information such as the number of dead or injured persons in terms of factors influencing the various crash types. The method proposed to this aim is the logistic regression, which is able to determine the weight of each factor involved in the analysis on accident severity. The emerging results suggest that there are interesting differences between accidents type. The findings show that logistic regression as used in this research is a capable tool in providing significant interpretations that can be used for future safety improvements in Italy.
... Third, the characteristics of the original crash data were not fully considered and thus more potential data processing approaches would facilitate a more comprehensive and in-depth research. It is worth noting that the psychological state of the driver, driving habits, and smart techniques should be integrated into the data analysis and countermeasures suggestions in future research [29][30][31]. ...
The occurrence and severity of truck crashes generally involve complex interactions among factors correlated to driver characteristics, vehicle attributes, roadway geometry and environment conditions. Thus, the elucidation of the significance of these potential contributory factors is critical when developing safety improvement countermeasures. To this end, data from a total of 1175 crashes involving at least one large truck and collected between 2010 and 2015 from two typical freeways in mountainous areas in Jiangxi and Shaanxi (China), were analyzed using a partial proportional odds model to determine the significant risk factors for injury severity of these crashes. Fourteen total explanatory variables, including the age of the driver, seatbelt status, number of vehicle involved, type of transport, freight conditions, brake system status, disregarding speed limit or not, following distance, horizontal roadway alignment, vertical roadway alignment, seasons, day of week, time of crash, and weather were found to significantly affect the severities of the truck crashes. In addition, old drivers, involvement of multiple vehicles, failure to wear seatbelts, overloading, speeding, brake failure and risky following behavior, curve section, seasons (summer, autumn and winter), nighttime period, and adverse weather conditions were also found to significantly increase the likelihood of injury and fatality crashes. Taken together, these findings may serve as a useful guide for developing legislation and technical countermeasures to ensure truck safety on freeways in mountainous regions, particularly in the context of a developing country.
... This measure is based on qualitative-based scales, either numerical or Likert-based [10,[31][32][33]. As a consequence, most statistical methods used in the subsequent data analysis to model user satisfaction are: (1) those methods which relate an ordinal scale to a continuous variable, including regression analysis [15,34], structural equation systems [35][36][37] and factorial analysis [38,39]; and (2) methods dealing with satisfaction or perception as a discrete variable, such as logit models [40,41] and ordered logit and probit models [8,19,42,43]. ...
Knowing public transport service's user satisfaction is essential to maintaining and increasing its quality and demand. Several studies have analysed the factors influencing users' satisfaction, considering their perceptions of specific attributes of the service. However, other aspects, such as the spatial distribution of users, i.e., their origin neighbourhoods, could significantly affect their satisfaction with the service, showing social inequity patterns. This paper proposes a new methodology to evaluate whether these spatial differences in satisfaction exist. Using the city of Santander (Spain) as an example, ordered probit models have been estimated, linking the bus users' overall satisfaction with variables that include their perceptions of the service and socio-demographic characteristics, and with dummy variables which classify each trip according to its neighbourhood origin. Our results confirm the existence of variations in satisfaction depending on the area of the city under study. In addition, user characterization variables, such as age, which were not significant when considering the city as a whole, proved to be influential in some areas. The estimated model, considering spatial differences, had a higher goodness of fit than that of models not taking zoning into account, and reproduced the overall satisfaction pattern presented in the study area with less error. The consideration of spatial differences in the modelling process enabled the detection of priority areas in which to implement measures to improve service quality and equity, thus increasing the use of public transport and supporting the promotion of a more sustainable mobility.
... For these reasons, in this paper we want to investigate on the appropriateness of the Ordered Probit (OP) modelling to analyse how passengers' perceptions about the service quality factors influence directly passengers' behavioural intentions towards the use of transit services. Authors of the proposed paper have a certain experience in OP models because in the past they adopted OP models for analysing service quality of airport transit services (Eboli and Mazzulla, 2009), and also in the field of road safety (Cardamone et al., 2016(Cardamone et al., , 2017de Oña et al., 2014). In this paper, the proposed methodology is tested by using data concerning the LRT of Seville (Spain). ...
Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company's service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose an ordered Probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future.
... For data treatment, the original Likert 5-point scale responses (1=total disagreement, 2=disagreement, 3=neutral opinion, 4=agreement, 5= total agreement), was reduced to a 3point scale (1=disagreement, 2=neutral opinion, 3=agreement) in order to simplify computation as well as the results, while giving a higher significance between opposite positions (Jeong, 2016). Due to the existence of ordinal variables, Ordered Regression Models (ORM) are indicated for analyzing this kind of data, as stated in the bibliography: Cardamone et al., (2016Cardamone et al., ( , 2017 and Eboli and Mazzulla (2009) for analyzing transport transit services quality, and de Oña et al., (2014) for a road safety study. There are two methods within the ORM: the Ordered Logit Models (OLM) and the Ordered Probit Models (OPM). ...
Between 2017 and 2018, a survey was carried out in Coimbra (Portugal) on the implementation of a new mass transport system. The analysis of this survey looks into the opinion regarding two key questions: the need for this new mode of transport, and how much support is behind investing to implement. The application of Ordered Logit Models has led to identify the variables that best explain the answers to these key questions. Moreover, the marginal effects of these variables were calculated, and the conditions increasing a tendency to answer contrary opinions (agreement/disagreement) to the key questions were identified. Finally, the working hypothesis —that if the new means of transport is considered necessary, then the investment is supported— was confirmed.
... However, there is no consensus on which model is the best. The authors of this paper have already adopted in the past OP models for analysing other transportation issues, as service quality of airport transit services (Eboli and Mazzulla, 2009), and road safety (Cardamone et al., 2015(Cardamone et al., , 2016de Oña et al., 2014). ...
The configuration of urban areas is the result of a cyclic relationship between land use and transportation system: the changes in transportation system arrangements influence the localisation of residence and economic activities, as well as the changes in land use affect transportation system characteristics. In this context, by operating on land use, travel demand can be shift from the individual transportation modes to transit systems. In the literature, many conceptual models were proposed to describe the complex relationship between land use and travel behaviour. In addition to spatial variation, the study of travel demand shows the categorical variation of variables.
... Since many years ago, several researchers have indicated that the results are similar; however, there is no consensus on which model is the best. The authors of this paper have already adopted in the past OP models for analysing service quality of airport transit services (Eboli and Mazzulla, 2009), and road safety (Cardamone et al., 2014, 2015, de Ona et al., 2014. In the OP model there is an observed ordinal variable Y, which is, in turn, a function of another variable Y * that is not measured (Borooah, 2001 threshold, as showed by the following formulas (1). ...
The configuration of urban areas is the result of a cyclic relationship between land use and transportation system: the changes in transportation system arrangements influence the localisation of residence and economic activities, as well as the changes in land use affect transportation system characteristics. In this context, by operating on land use, travel demand can be shift from the individual transportation modes to transit systems. In the literature, many conceptual models were proposed to describe the complex relationship between land use and travel behaviour. In addition to spatial variation, the study of travel demand shows the categorical variation of variables. This work aims to analyse the influence of the categorical variation of variables impacting on transit use. An ordered probit model is proposed for evaluating how transit use depends on variables related to socioeconomic characteristics of population, territorial features, accessibility, and transportation system. The study case is Madrid metro network (Spain). The results show a strong influence of characteristics of population and land use variables on daily trips made using metro system and highlighted the aspects that mainly impact on the choice to travel by metro, providing useful suggestions for shifting people from individual transportation mode to transit systems.
