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.

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 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.

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.

Assessment of Discretionary Lane-Changing Decisions using a Random Parameters Approach with Heterogeneity in Means and Variances

2021 ◽  
pp. 036119812199236
Author(s):  
Qianwen Li ◽  
Xiaopeng Li ◽  
Fred Mannering
Keyword(s):  
Traffic Flow ◽  
Unobserved Heterogeneity ◽  
Random Parameters ◽  
Lane Changing ◽  
Flow Models ◽  
Congested Traffic ◽  
Traffic Flow Models ◽  
The Mean ◽  
New Evidence ◽  
Vehicle Size

Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants of lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver acceleration/deceleration behavior, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, that the mean of this variation decreased with increasing average headway, and the variance increased with increasing driver acceleration/deceleration. These empirical findings provide interesting new evidence on the determinants of lane changing, which can be used in traffic flow models to better replicate and predict traffic flow.

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.

Simplified analysis of a stochastic system

1979 ◽  
Vol 44 (2) ◽  
pp. 328-339
Author(s):  
Vladimír Herles
Keyword(s):  
Statistical Analysis ◽  
Dynamic Behavior ◽  
Stochastic System ◽  
Additive Noise ◽  
Random Parameter ◽  
Systematic Difference ◽  
Order System ◽  
Constant Parameter ◽  
Random Parameters ◽  
Simplified Analysis

Contradictious results published by different authors about the dynamics of systems with random parameters have been examined. Statistical analysis of the simple 1st order system proves that the random parameter can cause a systematic difference in the dynamic behavior that cannot be (in general) described by the usual constant-parameter model with the additive noise at the output.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Examining drivers’ socio-demographic variables and perceptions towards sanction mechanisms on speeding behaviour on highways: targeting appropriate prevention

2021 ◽  
pp. 1-12
Author(s):  
Fitri Trapsilawati ◽  
Nadhiya Ulhaq Priatna ◽  
Titis Wijayanto ◽  
Ari Widyanti ◽  
Utami Dyah Syafitri ◽  
...  
Keyword(s):  
Traffic Accident ◽  
Online Survey ◽  
Demographic Variables ◽  
Targeted Prevention ◽  
Material Loss ◽  
Legal Sanction ◽  
Study Results ◽  
Legal Sanctions ◽  
Prevention Programmes ◽  
Car Driving

Abstract Investigating the underlying predictors of speeding behaviour deserves the full attention of research. This study aims to examine the effects of demographic variables on the perceived deterrent mechanisms and to predict speeding behaviour to target appropriate prevention programmes. In this study, 212 randomly selected drivers having a valid car driving licence participated in an online survey. The results revealed that demographic variables influenced drivers’ perceptions towards social and legal sanctions as well as material loss. The model revealed that two sanction-related constructs, that is, legal sanction (b = −0⋅227, P = 0⋅007) and material loss (b = −0⋅218, P = 0⋅005), as well as lax perception towards traffic accident (b = −0⋅176, P = 0⋅025), were the significant predictors of speeding behaviour. These findings suggested that prevention programmes should prioritise young and single drivers. The most effective targeted prevention programmes are highlighted accordingly based on the study results.

Modeling Safety Effects of Horizontal Curve Design on Injury Severity of Single-Motorcycle Crashes with Mixed-Effects Logistic Model

2017 ◽  
Vol 2637 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Chunfu Xin ◽  
Zhenyu Wang ◽  
Chanyoung Lee ◽  
Pei-Sung Lin
Keyword(s):  
Logistic Model ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Mixed Effects ◽  
Severe Injury ◽  
Horizontal Curve ◽  
Random Parameters ◽  
Horizontal Curves ◽  
Curve Design ◽  
Motorcycle Crashes

Horizontal curves have been of great interest to transportation researchers because of expected safety hazards for motorcyclists. The impacts of horizontal curve design on motorcycle crash injuries are not well documented in previous studies. The current study aimed to investigate and to quantify the effects of horizontal curve design and associated factors on the injury severity of single-motorcycle crashes with consideration of the issue of unobserved heterogeneity. A mixed-effects logistic model was developed on the basis of 2,168 single-motorcycle crashes, which were collected on 8,597 horizontal curves in Florida for a period of 11 years (2005 to 2015). Four normally distributed random parameters (moderate curves, reverse curves, older riders, and male riders) were identified. The modeling results showed that sharp curves (radius <1,500 ft) compared with flat curves (radius ≥4,000 ft) tended to increase significantly the probability of severe injury (fatal or incapacitating injury) by 7.7%. In total, 63.8% of single-motorcycle crashes occurring on reverse curves are more likely to result in severe injury, and the remaining 26.2% are less likely to result in severe injury. Motorcyclist safety compensation behaviors (psychologically feeling safe, and then riding aggressively, or vice versa) may result in counterintuitive effects (e.g., vegetation and paved medians, full-access-controlled roads, and pavement conditions) or random parameters (e.g., moderate curve and reverse curve). Other significant factors include lighting conditions (darkness and darkness with lights), weekends, speed or speeding, collision type, alcohol or drug impairment, rider age, and helmet use.

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