scholarly journals Development of Accident Frequency Models with Random Parameters on Interstate Roadway Segments with and without Lighting Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Traffic Accidents ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Random Parameters ◽  
Factors Affecting ◽  
Interstate Highways ◽  
Estimated Parameters ◽  
Fixed Parameter ◽  
Lighting Systems

This study explored factors affecting traffic accidents in roadway segments with and without lighting systems using a random parameter negative binomial model. This study sought to make up for a shortcoming of the fixed parameter model that constrained the estimated parameters to be fixed across observations, by applying random parameters that can take into account unobserved heterogeneity. Three variables had a random parameter among nine significant variables in segments with lighting systems, while seven of the eleven significant variables in a segment without a lighting system had random parameters. The different influence of interstate highway geometrics on vehicle crashes with and without lighting systems found through this study considering unobserved heterogeneity may hopefully help reduce accident frequencies and consider installation of lighting systems on interstate highways in the future.

scholarly journals Random Parameter Negative Binomial Model of Signalized Intersections

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee ◽  
Jinwoo Jeon
Keyword(s):  
Negative Binomial ◽  
Random Parameter ◽  
Signalized Intersections ◽  
Negative Binomial Model ◽  
Marginal Effect ◽  
Binomial Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Independent Variables ◽  
Insight Into

Factors affecting accident frequencies at 72 signalized intersections in the Gyeonggi-Do (province) over a four-year period (2007~2010) were explored using the random parameters negative binomial model. The empirical results from the comparison with fixed parameters binomial model show that the random parameters model outperforms its fixed parameters counterpart and provides a fuller understanding of the factors which determine accident frequencies at signalized intersections. In addition, elasticity and marginal effect were estimated to gain more insight into the effects of one-percent and one-unit changes in the dependent variable from changes in the independent variables.

Estimating Incident Duration Considering the Unobserved Heterogeneity of Risk Factors for Trucks Transporting HAZMAT on Expressways

2019 ◽  
Vol 2673 (2) ◽  
pp. 232-242 ◽  
Author(s):  
Jungyeol Hong ◽  
Reuben Tamakloe ◽  
Dongjoo Park ◽  
Yoonhyuk Choi
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Economic Losses ◽  
Random Parameters ◽  
Crash Data ◽  
Incident Duration ◽  
Fixed Model ◽  
Accident Response

Traffic accidents involving vehicles transporting hazardous materials (HAZMAT) on expressways not only delay traffic flow but can also cause large-scale casualties and socio-economic losses. Therefore, rapid response to and prevention of these accidents is important to minimize such loss. To ensure more efficient accident response, this study applied a random parameter hazard-based Weibull modeling approach to measure the relationship between crash characteristics and accident duration for trucks transporting HAZMAT. The study focuses on finding the key factors that have an impact on the accident duration of these vehicles as well as a statistical method to estimate the accident duration. The analysis is based on raw crash data from 2007 to 2017, obtained from the Korea Expressway Corporation, of crashes that involved HAZMAT trucks. The study found that crashes occurring during peak times of the day; crashes occurring on segments at the mainline, ramp, and roadways with a guardrail; and the number of vehicles involved in a crash, result in random parameters. In addition, the weather, season, crash severity, truck size, crash location, type of accident report, roadside features (e.g., guardrails), and status after a crash, can be used to explain the accident duration. The random parameters hazard-based model is found to have a better fit than a fixed model since it is able to capture the unobserved heterogeneity in the hazard function.

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

scholarly journals Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

2019 ◽  
Vol 16 (14) ◽  
pp. 2632 ◽  
Author(s):  
Chen ◽  
Song ◽  
Ma
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Seat Belt ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Bivariate Ordered Probit ◽  
Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

scholarly journals Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chenzhu Wang ◽  
Fei Chen ◽  
Jianchuan Cheng ◽  
Wu Bo ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Traffic Volume ◽  
Crash Risk ◽  
Operating Speed ◽  
Contributing Factors ◽  
Road Geometry ◽  
Elasticity Effects

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

Analysis of Rural Interstate Fatality Rates in Consideration of Recent Increases in Maximum Statutory Speed Limits

2019 ◽  
Vol 2673 (11) ◽  
pp. 48-58
Author(s):  
Jacob Warner ◽  
Hitesh Chawla ◽  
Chao Zhou ◽  
Peter T. Savolainen
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
State Level ◽  
Random Parameter ◽  
Maximum Speed ◽  
Speed Limit ◽  
Speed Limits ◽  
Fatal Crashes ◽  
Significant Research

The relationship between traffic safety and speed limits has been an area of significant research. Since the repeal of the National Maximum Speed Law in 1995, states have full autonomy in establishing maximum statutory speed limits. Since 2001, at least 25 states have increased their maximum limits to speeds as high as 85 mph. This study examines changes in rural interstate fatalities from 2001 to 2016 in consideration of such increases. Speed limit policy data include the maximum speed limit for each state–year combination, as well as the proportion of rural interstate mileage posted at each speed limit in each state. Random parameter negative binomial models are estimated to control for unobserved heterogeneity, as well as time-invariant effects unique to each state. The results show that increasing the mileage of rural interstates posted at 70, 75, or 80 mph by 1% is associated with fatality increases of 0.2%, 0.5%, and 0.6%, respectively. These increases are more pronounced than when considering only the maximum statutory limits in each state. The study also examines the influence between these higher limits and the frequency of fatal crashes involving speeding and driver distraction. At the highest limits of 75 and 80 mph, the increases among these subsets of crashes are greater than the increases in total fatalities. Ultimately, this study provides important empirical evidence in support of continuing speed limit policy discussions, in addition to identifying salient analytical concerns that should be considered as a part of longitudinal analyses of state-level fatality data.

scholarly journals Trend of Mortality Rate Due To Traffic Accidents In Iran From 2006 to 2018

2021 ◽  
Author(s):  
Saeed Erfanpoor ◽  
Jalil Hasani ◽  
Seyed Davood Mirtorabi ◽  
Reza Haj Manouchehri ◽  
Seyed Saeed Hashemi Nazari
Keyword(s):  
Mortality Rate ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Epidemiological Studies ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Common Causes ◽  
Causes Of Mortality

Abstract Background: Traffic accidents are one of the most common causes of mortality and physical disabilities, endangering the lives of many people all over the world annually and are among the top public health problems worldwide. In the present study, we aimed to investigate the trend of mortality rate due to traffic accidents in the provinces of Iran.Methods: In this cross-sectional study, all the deaths caused by traffic accidents in Iran during 2006-2018 were investigated. Using the population of the country by age, sex, and provinces of the country, the mortality rate was calculated and the trend of 13-year changes was studied. The negative binomial regression was used to analyze the linear or nonlinear trend of reduction in mortality rate during the study years. Microsoft Excel 2016 and Stata version 14 software were used to analyze the data.Results: During the study period, 259995 traffic accidents deaths occurred in Iran, of which 78.6% were men and 21.4% were women. The mean age of the deceased was 37.6 ± 20.7 years (37.4 ± 20 years in men and 38.6 ± 23 years in women). The number of the deaths in these years has decreased from 27,567 in 2006 to 17,183 in 2018 and the mortality rate has dropped from 39 per 100,000 in 2006 to 21 per 100,000 in 2018.Conclusion: Despite the decreasing trend in the mortality rate of traffic accidents in Iran during the study years, this trend was different across the provinces. Therefore, it seems necessary to design epidemiological studies to be conducted in different area and provinces of a country, to better and more accurately determine the factors affecting the occurrence of these deaths.

A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

2020 ◽  
Vol 2674 (7) ◽  
pp. 312-322
Author(s):  
Xiaoyan Huo ◽  
Junqiang Leng ◽  
Qinzhong Hou ◽  
Hao Yang
Keyword(s):  
Frequency Analysis ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Good Alternative ◽  
Omitted Variables ◽  
Random Parameters ◽  
Safety Factors ◽  
Crash Frequency ◽  
Study Results ◽  
Different Sources

Unobserved heterogeneity induced by omitted variables is a major challenge in developing reliable road safety models. In recent years, the random parameters negative binomial (RPNB) model has been used frequently in crash frequency analysis to account for unobserved heterogeneity. However, the majority of past studies of the RPNB model assumed that there was no correlation between different sources of unobserved heterogeneity, which is not always true given the complex interactions of safety factors. Compared with the RPNB model, a more flexible random parameters model that is the correlated random parameters negative binomial with heterogeneity in means (CRPNBHM) model was proposed in this study. Results indicate that the CRPNBHM model could not only capture the otherwise unobserved heterogeneity, but also track the underlying correlation among different sources of unobserved heterogeneity, thus outperforming the RPNB model. In addition, new insights into the interactions of safety factors (e.g., the joint safety effects of heavy trucks and pavement rutting depth) were obtained from the CRPNBHM model and these are expected to be beneficial in developing effective safety countermeasures. Results from this study demonstrated the CRPNBHM model to be a good alternative for crash frequency analysis, particularly when unobserved heterogeneity was detected.

scholarly journals An Investigation of the Safety Performance of Roundabouts in Korea Based on a Random Parameters Count Model

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee ◽  
Je-Jin Park
Keyword(s):  
Traffic Flow ◽  
Traffic Accident ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Random Parameters ◽  
Flow Conditions ◽  
Count Model ◽  
Accident Frequency ◽  
Study Results ◽  
Accident Data

It is well known that a roundabout is an efficient and safe intersection. However, the safety is generally influenced by the given various conditions. This study analyzed the effects of the geometric and traffic flow conditions on traffic accident frequency at roundabouts, constructed in Korea since 2010. Many previous studies have investigated the efficiency and safety effects of roundabout installation. However, not many studies have analyzed the specific influences of individual geometric elements and traffic flow conditions of roundabouts. Accordingly, this study analyzed the effects of various influencing variables on traffic accident frequency based on a random parameter count model using traffic accident data in 199 roundabouts. Using random parameters that can take into account unobserved heterogeneity, this study tried to make up for the weakness of the fixed parameters model, which constrains estimated parameters to be fixed across all observations. A total of eight variables were determined to be the main influencing factors on traffic accident frequency including the number and width of entry lanes, the presence of pedestrian crossings, the width of the circulatory lanes, the presence of central islands, the radius and number of entry lanes, and traffic volume influence accident frequency. Based on the study results, safer roundabout design and more efficient roundabout operation are expected.

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