Estimating an Injury Crash Rate Prediction Model based on severity levels evaluation: the case study of single-vehicle run-off-road crashes on rural context (original) (raw)
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The Baltic Journal of Road and Bridge Engineering, 2014
The object of this research is to develop one and only injury crash rate prediction model differentiable for three main crash types (head-on/side collisions, rear-end collisions, single-vehicle runoff road crashes) observed on the selected Italian two-lane rural roads in low-volume conditions. An explanatory variable reflecting road "Surface" conditions (dry/wet), "Light" conditions (day/night), and geometric "Element" (tangent segment/circular curve) when the crash happened and referred to the police reports has been proposed within the safety performance function all together (Surface, Light and Element) with three other significant variables (lane width, horizontal curvature indicator and mean speed) as consistent factors to predict crashes and their degree of seriousness for different kind of crashes. Among different statistical approaches introduced in the past few years to deal with the data and methodological issues associated with crash-frequency data, a generalized estimating equation has been implemented to take into account over-dispersion of the crash data, with a negative binomial distribution additional log linkage equation. Residual plots were combined with the validation procedure and other goodness-of-fit measurements to determine the reliability of the results. Potential countermeasures have been proposed for the critical crash types surveyed on the studied roads; these countermeasures have had positive effects on the road segments where the serious crash types have occurred over an eight-year period of analysis.
Safety and Security Engineering V, 2013
The research presented here is addressed to develop only one safety performance function (SPF) from the perspective of driver gender for three identified main crash types (head-on/side collisions, rear-end collisions, single-vehicle run-offroad crashes) that is able to predict the injury crash rate on low-volume roads. According to the crash police reports, it came in sight that males and females differ in terms of their psychological attributes and, consequently, their response to the crash risk can change producing different effects on the severity. The analysis was divided into two phases: the first deals with SPF calibration, while the second concerns SPF validation. A total length of 355 km was used in the first phase involving 5 years of the crash database (2003-2007), to a total of 95 injury crashes which led to 136 injuries (63% male only drivers, 8% female only drivers and 29% female+male drivers) and 9 deaths (78% male only drivers and 22% female+male drivers). A total length of 295 km was used in the second phase involving 3 years of the crash database (2008-2010), to a total of 73 injury crashes which led to 120 injuries (68% male only drivers, 4% female only drivers and 28% female+male drivers) and 4 deaths (75% male only drivers and 25% female+male drivers). GEE was adopted to calibrate SPF. Mean width, mean speed at each analyzed road segment, and a numerical variable "SLEH" reflecting the identified road "Surface" (dry/wet), "Light" conditions (day/night), geometric "Element" (tangent segment/circular curve) and "Human" factors (gender/age/number drivers) all together when the crash happened, were introduced in the predictive safety model looking toward gender and age drivers.
Develop Calibration Factors for Crash Prediction Models for Rural Two-Lane Roadways in Albania
2017
This paper documents the development of calibration factors for crash prediction models for rural two-lane roadways in Albania. The crash prediction modes in the Interactive Highway Safety Design Model were developed using data from multiple states, therefore the models must be calibrated to account for local factors, such as weather, roadway conditions, and drivers’ characteristics. In this study, the calibration factors were developed to give a better prediction of crash frequencies on rural two lane roadways in Albania. Keywords—Component; Highway Safety Manual, Safety, Calibration Factor, Safety Performance Function
Injury severity models for motor vehicle accidents: a review
Proceedings of the ICE - Transport, 2013
Modelling of traffic accidents injury severity is a complex task. In the last few years the number and variety of studies that analyse injury severity of traffic accidents have increased considerably. In this paper 19 modelling techniques used to model injury severity of traffic accidents where at least a 4-wheeled vehicle is involved have been analysed. The analysis and the comparison between models was performed based on seven criteria (modelling technique, number of records, number of variables, area type, features, injury level and model fit). In general, it is not possible to recommend a method that could be identified as the best one. Each modelling technique has its own limitations and characteristics, awareness of which will help analysts to decide the best method to be used in each particular modelling problem. However, some general conclusions can be established: in most cases the results of models' fits are found to be satisfactory, though not excellent; in the case of data mining models, accuracy improves with balanced datasets; and no correlation was found to exist between the number of accident records and the number of analysed variables.
A crash-prediction model for multilane roads
Accident Analysis & Prevention, 2007
Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.
A Review of Road Crash Prediction Models for Developed Countries
Journal of Traffic and Transportation Engineering, 2017
Road Crash losses have been on an growing trend for the preceding decade or so in India. consequently traffic safety organization has emerged as a topic of argument for researchers all over the world. For this reason Crash modeling on different factors causing them has to be conducted. Crash modelling helps to anybody to recognize the real causative agents behind an accident to occur. The effect of one cause can be greater than the other. And those causes can only be known from Crash modelling. In this paper it is tried try to divide this Crash modelling techniques into different categories based on the road geometrics characteristics, traffic characteristics and Environmental factors on urban roads and on rural roads of different developed countries. In both urban and rural road crash studies it can be seen that for the most part regression techniques like linear, multi-linear, logit and poisons regression were used for modelling the road crashes. It was also noticeable that freque...
Factors affecting road crash modeling
Keywords: factors affecting accidents accidental study models logistic regression statistics Road accident fatalities have been on an increasing trend for the last decade or so in India. Hence traffic safety management has emerged as a topic of discussion for researchers all over the world. Hence accident modelling on different factors causing them has to be conducted. Accident modelling helps us to know the real causative agents behind an accident to occur. The effect of one cause can be greater than the other. And those causes can only be known from accident modelling. In this paper we have tried to divide this accident modelling techniques into two different categories based on the location of road i.e. accidents on urban roads and on rural roads. In both urban and rural road accident studies it was seen that mainly regression techniques like linear, multi-linear, logit and poisons regression have been used for modelling the road crashes. It was also marked that mostly authors have tried to research on one cause and go deep into it rather considering all factors at a time. From the studies it was found that speed and age along with gender has been the area of study for accident causes in urban areas whereas in rural roads mostly all authors have limited their studies to speed on roads and has been noted as the major cause of accidents in rural areas. This paper has tried to review as much papers as possible and various gaps in research along with future scope of study in this area has been indicated. Starting from the basic models like negative binomial/Poisson's model to the logistic and linear regressions to the new modeling techniques involving genetic mining and fuzzy logics have been discussed explicitly in the paper.
Sustainability
The sustainable development goals “Good health and well-being” and “Sustainable cities and communities” of the United Nations and World Health Organization, alert governments and researchers and raise awareness about road safety problems and the need to mitigate them. In Portugal, after the economic crisis of 2008–2013, a significant amount of road assets demand investment in maintenance and rehabilitation. The areas where these actions take place are called work zones. Considering the particularities of these areas, the proposed work aims to identify the main factors that impact the occurrence of work zones crashes. It uses the statistical technique of multinomial logistic regression, applied to official data on road crashes occurred in mainland Portugal, during the period of 2010–2015. Usually, multinomial logistic regression models are developed for crash and injury severity. In this work, the feasibility of developing predictive models for crash nature (collision, run off road a...
Factors related to highway crash severity in Brazil through a multinomial logistic regression model
TRANSPORTES
Reducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a multinomial logistic regression model is fitted to nationwide highway crash data in Brazil from 2017 to 2019 to identify and estimate the associated factors to crash severity. Severity is classified as without injury, with injured victims or with fatal victims. Amongst other observations, results indicate that pedestrian involvement in highway crashes increase dramatically their severity. Also, conditions that favor greater speeds (clear weather, times when there are fewer vehicles on the road) are also related to an increase in crash severity, pointing to a proportional relation with traffic fluidity. Moreo...