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Accident Frequency

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Stephen Ison – One of the best experts on this subject based on the ideXlab platform.

  • predicting Accident Frequency at their severity levels and its application in site ranking using a two stage mixed multivariate model
    Accident Analysis & Prevention, 2011
    Co-Authors: Chao Wang, Mohammed A. Quddus, Stephen Ison

    Abstract:

    Abstract Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying Accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of Accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate Accident Frequency at different severity levels, namely the two-stage mixed multivariate model which combines both Accident Frequency and severity models. The Accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for Accident Frequency and severity analysis respectively, and the results combined to produce estimation of the number of Accidents at different severity levels. Based on the results from the two-stage model, the Accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naive ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional Frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual Accident level data and is able to predict low Frequency Accidents (such as fatal Accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting Accident Frequency according to their severity levels and site ranking.

  • Prediction of Accident Frequency at Their Severity Levels and Its Application in Site Ranking Using Two-Stage Mixed Multivariate Model
    , 2011
    Co-Authors: Chao Wang, Mohammed A. Quddus, Stephen Ison

    Abstract:

    Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying Accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson) for modeling the number of Accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate Accident Frequency at different severity levels, namely the two-stage mixed multivariate model which combines both Accident Frequency and severity models. The Accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for Accident Frequency and severity analysis respectively, and the results combined to produce estimation of the number of Accidents at different severity levels. Based on the results from the two-stage model, the Accident hotspots on the M25 and surround have been identified. Compared to the traditional Frequency based analysis, the two-stage model has the advantage in that it utilizes more detailed individual Accident level data and it is able to predict low Frequency Accidents (such as fatal Accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting Accident Frequency according to their severity levels and site ranking.

Fred L. Mannering – One of the best experts on this subject based on the ideXlab platform.

  • The effect of ice warning signs on ice-Accident frequencies and severities
    Accident; analysis and prevention, 2001
    Co-Authors: Jodi Carson, Fred L. Mannering

    Abstract:

    Signing of non-permanent road surface conditions, such as ice, is difficult because hazard formation, location, and duration are unpredictable. Subsequently, many state transportation departments have begun to question the sensibility of expending material and personnel resources to maintain ice warning signs when little proof exists of their effectiveness in improving highway safety. This research statistically studies the effectiveness of ice warning signs in reducing Accident Frequency and Accident severity in Washington State. Our findings show that the presence of ice warning signs was not a significant factor in reducing ice-Accident Frequency or ice-Accident severity. However, we were able to identify significant spatial, temporal, traffic, roadway and Accident characteristics that influenced ice-Accident Frequency and severity. The identification of these characteristics will allow for better placement of ice warning signs and improvements in roadway and roadside design that can reduce the Frequency and severity of ice-related Accidents.

  • ANALYSIS OF ROADSIDE Accident Frequency AND SEVERITY AND ROADSIDE SAFETY MANAGEMENT
    , 1999
    Co-Authors: Jong Jae Lee, Fred L. Mannering

    Abstract:

    In Washington State, priority programming for evaluating Accident prevention and mitigation (safety improvement) involves analysis of roadside features, but the effects that such features have on the Frequency and severity of Accidents is not well understood. This study investigated the relationships among roadway geometry, roadside characteristics, and run-off-roadway Accident Frequency and severity to provide a basis for identifying cost-effective ways to improve highway designs that will reduce the probability of vehicles leaving the roadway and the severity of Accidents when they do. To better understand the effects of roadside features on Accident Frequency and severity, the researchers surveyed other states’ priority programming practices. The survey showed that proactive approaches, in general, are in their infancy, and none of them adequately accounts for the effects of roadside features on Accidents. To quantify the effects of roadside features on Accident Frequency and severity, the researchers gathered data from the northbound direction of State Route 3 in Washington State. For Accident Frequency analysis, negative binomial and zero-inflated negative binomial models of monthly Accident Frequency were estimated. The findings showed both significant differences and similarities in the factors that affect urban and rural Accident frequencies. The results indicated that run-off-roadway Accident frequencies can be significantly reduced by increasing lane and shoulder widths; widening medians; expanding approaches to bridges; shielding, relocating, and removing roadside hazardous objects; and flattening side slopes and medians. The statistical analysis also provided an estimate of the magnitude of the influence of these factors. The effects of roadside features on run-off-roadway Accident severity were studied with a nested logit model. Roadside features that were found to significantly affect the severity of run-off-roadway Accidents included bridges, cut-type slopes, ditches, culverts, fences, tree groups, sign supports, utility poles, isolated trees, and guardrails. As was the case for the Frequency analysis, elasticity estimates allowed quantification of the effects of roadside features on Accident severity.

  • MODELING Accident FREQUENCIES AS ZERO-ALTERED PROBABILITY PROCESSES : AN EMPIRICAL INQUIRY
    Accident; analysis and prevention, 1997
    Co-Authors: Viswanathan Shankar, John Milton, Fred L. Mannering

    Abstract:

    This paper presents an empirical inquiry into the applicability of zero-altered counting processes to roadway section Accident frequencies. The intent of such a counting process is to distinguish sections of roadway that are truly safe (near zero-Accident likelihood) from those that are unsafe but happen to have zero Accidents observed during the period of observation (e.g. one year). Traditional applications of Poisson and negative binomial Accident Frequency models do not account for this distinction and thus can produce biased coefficient estimates because of the preponderance of zero-Accident observations. Zero-altered probability processes such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) distributions are examined and proposed for Accident frequencies by roadway functional class and geographic location. The findings show that the ZIP structure models are promising and have great flexibility in uncovering processes affecting Accident frequencies on roadway sections observed with zero Accidents and those with observed Accident occurrences. This flexibility allows highway engineers to better isolate design factors that contribute to Accident occurrence and also provides additional insight into variables that determine the relative Accident likelihoods of safe versus unsafe roadways. The generic nature of the models and the relatively good power of the Vuong specification test used in the non-nested hypotheses of model specifications offers roadway designers the potential to develop a global family of models for Accident Frequency prediction that can be embedded in a larger safety management system.

Chao Wang – One of the best experts on this subject based on the ideXlab platform.

  • predicting Accident Frequency at their severity levels and its application in site ranking using a two stage mixed multivariate model
    Accident Analysis & Prevention, 2011
    Co-Authors: Chao Wang, Mohammed A. Quddus, Stephen Ison

    Abstract:

    Abstract Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying Accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of Accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate Accident Frequency at different severity levels, namely the two-stage mixed multivariate model which combines both Accident Frequency and severity models. The Accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for Accident Frequency and severity analysis respectively, and the results combined to produce estimation of the number of Accidents at different severity levels. Based on the results from the two-stage model, the Accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naive ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional Frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual Accident level data and is able to predict low Frequency Accidents (such as fatal Accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting Accident Frequency according to their severity levels and site ranking.

  • Prediction of Accident Frequency at Their Severity Levels and Its Application in Site Ranking Using Two-Stage Mixed Multivariate Model
    , 2011
    Co-Authors: Chao Wang, Mohammed A. Quddus, Stephen Ison

    Abstract:

    Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying Accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson) for modeling the number of Accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate Accident Frequency at different severity levels, namely the two-stage mixed multivariate model which combines both Accident Frequency and severity models. The Accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for Accident Frequency and severity analysis respectively, and the results combined to produce estimation of the number of Accidents at different severity levels. Based on the results from the two-stage model, the Accident hotspots on the M25 and surround have been identified. Compared to the traditional Frequency based analysis, the two-stage model has the advantage in that it utilizes more detailed individual Accident level data and it is able to predict low Frequency Accidents (such as fatal Accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting Accident Frequency according to their severity levels and site ranking.