Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes

Bakhshi, Arash Khoda; Ahmed, Mohamed M.

doi:10.1016/j.aap.2020.105855

Cited by 31 publications

(14 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, some other contributing factors, including driver's fault and action at the time of the crash, are potentially available in detail as part of the crash reports, so we could extract them in order to develop more reliable models using them as descriptive variables in future studies. Statistically more advanced models, such as Bayesian hierarchical regression models [77], could also be applied in future work to observe and account for heterogeneity. The effectiveness of driver education, especially through supervised practice [78] before independent driving licensure and licensing policies are two primary preventive counter-measures that may help decrease young-driver crash risks, which is a good direction for future work.…”

Section: Limitations and Future Workmentioning

confidence: 99%

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

et al. 2022

View full text Add to dashboard Cite

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

show abstract

Section: Limitations and Future Workmentioning

confidence: 99%

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In addition to the considered SMoS, speed harmonization in four WZ areas has been investigated under varying CV-MPRs. Speed harmonization plays a crucial role in road safety by reducing speed variability and alleviating crash-prone conditions ( 88 – 92 ). During the simulation run, the speeds of individual vehicles in the traffic stream were observed using several data collection points located at 0.1 mi intervals in all individual WZ areas.…”

Section: Resultsmentioning

confidence: 99%

“…Interstate-80 (I-80) in Wyoming is a rural corridor with a relatively low volume of traffic that mostly operates at Level of Service A or B. I-80 is characterized by high truck volumes and continual adverse weather conditions, including foggy conditions, imposing significant safety concerns (17)(18)(19)(20)(21)(22)(23)(24)(25)(26). In line with the Connected Vehicle Pilot Deployment program on I-80 in Wyoming (Wyoming DOT CV Pilot) (27), this study investigates the safety performance of CVs on rural WZs during limited visibility caused by foggy conditions using VISSIM microsimulation modeling based on four sequential areas, including the advance warning, transition, activity, and termination area (28).…”

mentioning

confidence: 99%

Safety Impact of Connected Vehicles on Driver Behavior in Rural Work Zones under Foggy Weather Conditions

Adomah

Bakhshi

Ahmed

2021

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

Work zone safety is one of the paramount goals of the safety community. Safety in WZs is a particular concern under foggy conditions as they represent an exogenous factor contributing to high variability in driver behavior. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) applications on driver behavior during foggy weather conditions. A work zone (WZ) was simulated using VISSIM in four sequential areas, including the advance warning, transition, activity, and termination area. The effect of drivers’ increased situational awareness under the effect of WZW was calibrated in VISSIM based on the results of a high-fidelity driving simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Time Exposed Time-to-collision (TET), Time-Integrated Time-to-collision (TIT), and Modified Deceleration Rate to Avoid Crash (MDRAC), were employed to quantify the safety performance of CVs under varying CV Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that an increase in CV-MPR enhances the safety performance of the WZ area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR of up to 60% on I-80 would reduce mean speeds and the standard deviation of speed at each of the WZ areas, leading to more speed harmonization and minimizing the crash risk in WZs.

show abstract

“…From a general perspective, three factors contribute to this socioeconomic concern: the human factor, environmental conditions, and vehicle characteristics ( 3 , 4 ). Although it is known that the human is the most influential crash contributing factor, vehicle characteristics and maneuverability also influence crash severity ( 5 – 8 ). Statistics showed that heavy truck crashes are more severe than passenger car collisions, imposing serious safety concerns for transportation agencies, specifically on rural highways with ascending heavy vehicle traffic volume such as Interstate-80 (I-80) in Wyoming ( 7 , 9 , 10 ).…”

mentioning

confidence: 99%

Driving Simulator Trajectory-Level Analysis of Truck Drivers’ Behavioral Alteration in Connected Vehicles Environment Under Fog with Complex Roadway Geometry

Bakhshi

Ahmed

2022

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

Foggy weather increases crash likelihood when coinciding with roadway geometry changes inconsistent with drivers’ expectations. The situation might be exacerbated for heavy trucks having to evade critical safety events because of the vehicles’ maneuverability limitations, imposing prime safety challenges on major freight corridors like Interstate-80 (I-80) in the U.S. Aligned with the connected vehicle (CV) pilot program on I-80 in Wyoming, this study intends to unveil how CV technology alleviates safety concerns in this regard. To this aim, a with/without analysis approach was performed utilizing a high-fidelity truck driving simulator. Twenty-three professional truck drivers were recruited to drive the simulator in CV scenario with traveler information messages, including foggy weather ahead and an advisory speed of 45 mph, and in a non-CV counterpart without notifications. Longitudinal and lateral drivers’ behaviors were quantified by kinematic-based surrogate measures of safety (K-SMoS) characterized on vehicles’ trajectory, including longitudinal speed, lateral speed, steering, their corresponding spatial standard deviations, and the coefficient of variation of longitudinal speed. The central tendency and dispersion of K-SMoS distributions were compared between CVs and non-CVs throughout the simulated roadway. Results showed immediate truck drivers’ compliance to CV notifications, which was more apparent in their longitudinal driving behaviors. On a horizontal curve with poor visibility, statistically significant reductions in central tendency and dispersion of K-SMoS distributions up to 67% in CVs were observed, minimizing the crash risk in CV environments. Besides, findings revealed that exposure to the CV notifications minimized drivers’ behavior uncertainty, manifesting in their improved situational awareness and enhancing the safety performance of the traffic stream.

show abstract

Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes

Cited by 31 publications

References 46 publications

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Safety Impact of Connected Vehicles on Driver Behavior in Rural Work Zones under Foggy Weather Conditions

Driving Simulator Trajectory-Level Analysis of Truck Drivers’ Behavioral Alteration in Connected Vehicles Environment Under Fog with Complex Roadway Geometry

Contact Info

Product

Resources

About