Quantifying effects of reverse linear perspective as a visual cue on vehicle and platoon crash risk variations in car-following using path analysis

2021 ◽  
Vol 159 ◽  
pp. 106215
Author(s):  
Naikan Ding ◽  
Zhaoyou Lu ◽  
Nisha Jiao ◽  
Zhiguang Liu ◽  
Linsheng Lu
Keyword(s):  
Path Analysis ◽  
Linear Perspective ◽  
Crash Risk ◽  
Visual Cue ◽  
Car Following

scholarly journals Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Naikan Ding ◽  
Linsheng Lu ◽  
Nisha Jiao
Keyword(s):  
Visual Perception ◽  
Traffic Flow ◽  
Crash Risk ◽  
Perceptual Information ◽  
Specific Flow ◽  
Car Following ◽  
Driving Behaviors ◽  
Level Data ◽  
Spatial Transferability ◽  
Model Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

Impact of heterogeneity of car-following behavior on rear-end crash risk

2019 ◽  
Vol 125 ◽  
pp. 275-289 ◽  
Author(s):  
Junjie Zhang ◽  
Yunpeng Wang ◽  
Guangquan Lu
Keyword(s):  
Crash Risk ◽  
Car Following

scholarly journals Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiangqiang Shangguan ◽  
Ting Fu ◽  
Junhua Wang ◽  
Rui Jiang ◽  
Shou’en Fang
Keyword(s):  
Comparative Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mixed Effects ◽  
Driving Safety ◽  
Crash Risk ◽  
Traffic Density ◽  
Car Following ◽  
The Impact

Traditional surrogate measures of safety (SMoS) cannot fully consider the crash mechanism or fail to reflect the crash probability and crash severity at the same time. In addition, driving risks are constantly changing with driver’s personal driving characteristics and environmental factors. Considering the heterogeneity of drivers, to study the impact of behavioral characteristics and environmental characteristics on the rear-end crash risk is essential to ensure driving safety. In this study, 16,905 car-following events were identified and extracted from Shanghai Naturalistic Driving Study (SH-NDS). A new SMoS, named rear-end crash risk index (RCRI), was then proposed to quantify rear-end crash risk. Based on this measure, a risk comparative analysis was conducted to investigate the impact of factors from different facets in terms of weather, temporal variables, and traffic conditions. Then, a mixed-effects linear regression model was applied to clarify the relationship between rear-end crash risk and its influencing factors. Results show that RCRI can reflect the dynamic changes of rear-end crash risk and can be applied to any car-following scenarios. The comparative analysis indicates that high traffic density, workdays, and morning peaks lead to higher risks. Moreover, results from the mixed-effects linear regression model suggest that driving characteristics, traffic density, day-of-week (workday vs. holiday), and time-of-day (peak hours vs. off-peak hours) had significant effects on driving risks. This study provides a new surrogate safety measure that can better identify rear-end crash risks in a more reliable way and can be applied to real-time crash risk prediction in driver assistance systems. In addition, the results of this study can be used to provide a theoretical basis for the formulation of traffic management strategies to improve driving safety.

scholarly journals Pengaruh Tingkat Percaya Diri CEO terhadap Risiko Penurunan Harga Saham dengan Efisiensi Investasi sebagai Mediasi

2021 ◽  
Vol 31 (10) ◽  
pp. 2490
Author(s):  
Nila Hiliyah Yusuf
Keyword(s):  
Path Analysis ◽  
Confidence Level ◽  
Work Experience ◽  
Stock Price ◽  
Manufacturing Sector ◽  
Investment Decisions ◽  
Crash Risk ◽  
Investment Efficiency ◽  
Analytical Technique ◽  
Research Period

CEO has a role to make investment decisions. Age, education and work experience are factors can effect the CEO's confidence level to making decisions. When the investment decisions biased and risk lowering the company's performance, there will be a CEO's decision to delay bad information. Long delays bad information will increase stock price crash risk on future. Therefore, the purpose of this study is to examine the effect of the CEO's confidence level on future stock price crash risk with investment efficiency as a mediation. The manufacturing sector is used as the object of research with 98 companies as research samples with a research period from 2011-2019. Path analysis was used as an analytical technique and Sobel test as a measure of mediation. The results of this study prove that investment efficiency is able to mediate the effect of CEO's confidence level on future stock price crash risk. Keywords: CEO's Confidence Level; Investment Efficiency; Future Stock Price Decrease Risk.

New time-based surrogate safety measure to assess crash risk in car-following scenarios

2014 ◽  
Vol 7 (4) ◽  
pp. 229-238 ◽  
Author(s):  
H. Behbahani ◽  
N. Nadimi ◽  
S.S. Naseralavi
Keyword(s):  
Safety Measure ◽  
Crash Risk ◽  
Car Following ◽  
New Time

Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions

2018 ◽  
Vol 2672 (19) ◽  
pp. 91-101 ◽  
Author(s):  
Md Sharikur Rahman ◽  
Mohamed Abdel-Aty ◽  
Ling Wang ◽  
Jaeyoung Lee
Keyword(s):  
Standard Deviation ◽  
Traffic Safety ◽  
Risk Index ◽  
Flow Characteristics ◽  
Crash Risk ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Car Following ◽  
Real Time Traffic ◽  
Surrogate Measures

This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.

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