scholarly journals Driving Risk Affected Areas and Distribution Function of Sharp Horizontal Curves of Expressway

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Xiao-fei Wang ◽  
Xin-wei Li ◽  
Ying Yan ◽  
Xin-sha Fu
Keyword(s):  
Distribution Function ◽  
Safety Management ◽  
Distribution Patterns ◽  
Heart Rate Change ◽  
Driving Safety ◽  
Plane Curves ◽  
Horizontal Curves ◽  
Driving Risk ◽  
Vehicle Acceleration ◽  
Adjacent Line

The average death and injury intensity on sharp horizontal curves (SHCs) are much higher than those of straight sections of the expressway in China. In this paper, the statistics of crashes from 2008 to 2012 on 2200 km expressways in Guangdong province are collected, and the relationships between the radius of plane curves and the crash rate are analyzed. After that, the curved expressway section with radius equal to or less than 1000 m is defined as SHCs. According to the results of the test of the operating speed, the heart rate change of drivers, and the vehicle acceleration, the distribution patterns of driving risks on the certain SHCs were theoretically analyzed. Hence, the driving risk affected areas on adjacent line units of SHCs are determined as 200 m sections before entering or after exiting the SHCs. Combining with surveyed data, the spatial distribution of crashes on SHCs is analyzed, and the driving risk distribution function of SHCs in expressway is finally deduced. The result of this research provides a theoretical basis to enhance expressway safety management and to improve the driving safety on SHCs.

scholarly journals Effect of High-Altitude Environment on Driving Safety: A Study on Drivers’ Mental Workload, Situation Awareness, and Driving Behaviour

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xinyan Wang ◽  
Wu Bo ◽  
Weihua Yang ◽  
Suping Cui ◽  
Pengzi Chu
Keyword(s):  
High Altitude ◽  
Situation Awareness ◽  
Mental Workload ◽  
Mediating Effect ◽  
Driving Safety ◽  
Risky Driving ◽  
Driving Behaviour ◽  
Driving Risk ◽  
Low Altitude ◽  
The Relationship

This study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA), and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 356 lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activities at low altitudes (low-altitude group) were compared and analyzed. The results suggest that the differences between the two groups are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation awareness is lower significantly. The possibility of risky driving behaviours for high-altitude group, especially aggressive violations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-altitude environment for lowlanders.

Contractor and supplier management: mitigating risk by better decision making

2012 ◽  
Vol 52 (2) ◽  
pp. 642
Author(s):  
Dag Yemenu ◽  
Richard Cerenzio
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Safety Management ◽  
Safety Data ◽  
Health And Safety ◽  
Leading Edge ◽  
Third Party ◽  
Driving Safety ◽  
Comprehensive Management ◽  
And Performance

Global industry trends show increased outsourcing of non-core business activities (i.e. construction, maintenance, engineering, etc.) to third-party contractors. Data from several industries show that contractors face 1.5–3 times higher incident rates than in-house employees. This extended abstract covers leading-edge approaches for managing contractor risk, presently implemented by organisations in the oil and gas, mining and manufacturing industries. Using a database of more than 35,000 contracting companies and 220 owner/operator companies, this extended abstract accumulates more than six years of extensive health and safety data to show trends associated with health and safety management and performance improvement. Using statistical analysis methods, actionable leading indicators and insightful trends are discussed. Best practices of contractor management and decision-making tools based on a comprehensive management-system approach to contractor-risk management are also examined. Discussed is a practical model to address the following key elements: Gathering, reviewing and verifying contractor information as part of the due-diligence process. Analysing leading and lagging performance indicators. Driving safety through feedback, benchmarking, and continual improvement.

scholarly journals Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

2019 ◽  
Vol 5 (1) ◽  
pp. 3 ◽  
Author(s):  
Paolo Intini ◽  
Nicola Berloco ◽  
Vittorio Ranieri ◽  
Pasquale Colonna
Keyword(s):  
Safety Management ◽  
Explanatory Variables ◽  
Safety Issues ◽  
Horizontal Curves ◽  
Safety Research ◽  
Run Off ◽  
History Of ◽  
Disproportionate Number ◽  
The One ◽  
National Road

(1) Run-off-road (ROR) crashes are a crucial issue worldwide, resulting in a disproportionate number of traffic deaths. In safety research, macro-level analysis on large datasets is usually conducted by linking explanatory variables to ROR crash frequency/severity. Micro-analysis approaches, like the one used in this study, are instead less frequent. (2) A comprehensive Italian Fatal + Injury (FI) crash dataset was filtered to identify two-way two-lane rural road curves on the national road network on which more than one ROR FI crash (i.e., at least two crashes) in the observation period of four years had occurred. The typical features of the ROR FI crashes and the recurrent geometric (characteristics of tangents and curves) and operational features (inferred speeds, acceleration/decelerations) of the crash sites were reconstructed. (3) The main contributory factors in ROR FI crashes are: wet pavements, speeding, and distraction. Sites with a relevant history of ROR FI crashes present recurrent safety issues such as inadequate horizontal curve coordination, an insufficient tangent length for decelerating, and inferred operating speeds comparable/higher than the inferred design speeds. (4) Based on findings, some practical suggestions for road safety management and maintenance are proposed through specific indicators and countermeasures (speed, perception, and friction related).

scholarly journals The Driving Risk Analysis and Evaluation in Rightward Zone of Expressway Reconstruction and Extension Engineering

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chi Zhang ◽  
Bo Wang ◽  
Shaoxiang Yang ◽  
Min Zhang ◽  
Quanli Gong ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Safety Management ◽  
Evaluation Model ◽  
Risk Index ◽  
Traffic Volume ◽  
Entropy Method ◽  
Simulation Software ◽  
Evaluation Index ◽  
Transition Section ◽  
Driving Risk

In the expressway reconstruction and expansion engineering, the form of rightward zone is increasingly adopted, and its complicated traffic conditions can easily cause traffic accidents. In order to quickly and effectively grasp the traffic risk of the right diversion section, this study employs average speed, the coefficient of variation, the equivalent minimum safety distance, and the deceleration as evaluation indexes of driving risk, and then analyses the influence rules of traffic volume, the proportion of large vehicles, and the length of the transition section on each evaluation index by using Vissim simulation software. On the basis of this, we determine the weight of each evaluation index by the entropy method and establish the driving risk index evaluation model of the work zone with multiple linear regression. The results show that the partial regression coefficients of traffic volume, the proportion of large vehicles, and the length of the transition section to the driving risk index are 0.059, 0.317, and 0.15, respectively. Finally, in this paper, we analyze the traffic risk of example section based on the driving risk evaluation model. The results of evaluation are consistent with the number of measured conflicts. This study proposes a new method for predicting the traffic risk of the expressway reconstruction and extension engineering, which can provide a reference for the development of safety management measures in the rightward zone.

scholarly journals A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology

2018 ◽  
Vol 32 (11) ◽  
pp. 1850037 ◽  
Author(s):  
Chuan Sun ◽  
Chaozhong Wu ◽  
Duanfeng Chu ◽  
Zhenji Lu ◽  
Jian Tan ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Driving Safety ◽  
Rule Base ◽  
Driver Assistance Systems ◽  
Recognition Model ◽  
Time Operation ◽  
Belief Rule Base ◽  
Driving Risk ◽  
Data Driven Modeling

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

scholarly journals Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

2021 ◽  
Vol 11 (24) ◽  
pp. 12137
Author(s):  
Fei-Xue Wang ◽  
Qian Peng ◽  
Xin-Liang Zang ◽  
Qi-Fan Xue
Keyword(s):  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Hierarchical Control ◽  
Driving Safety ◽  
Cruise Control ◽  
City Bus ◽  
Tracking Ability ◽  
Vehicle Acceleration ◽  
Energy Economy ◽  
Road Slope

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

Personalized Subjective Driving Risk: Analysis and Prediction

2020 ◽  
Vol 32 (3) ◽  
pp. 503-519
Author(s):  
Naren Bao ◽  
Alexander Carballo ◽  
Chiyomi Miyajima ◽  
Eijiro Takeuchi ◽  
Kazuya Takeda ◽  
...  
Keyword(s):  
Risk Factors ◽  
Short Term Memory ◽  
Risk Model ◽  
Influential Factors ◽  
Driving Safety ◽  
Risk Level ◽  
Subjective Perception ◽  
Lane Changes ◽  
Driving Risk ◽  
Subjective Risk

Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.

Driver Workload and Secondary Task Modality while Driving on Horizontal Curves

2017 ◽  
Vol 61 (1) ◽  
pp. 1763-1767
Author(s):  
Heejin Jeong ◽  
Yili Liu
Keyword(s):  
Secondary Task ◽  
Motor Vehicle ◽  
Ordinary Least Squares ◽  
Influential Factor ◽  
Driving Safety ◽  
Steering Wheel ◽  
Horizontal Curves ◽  
Driver Workload ◽  
Road Geometry ◽  
Task Modality

High driver workload has been considered as a risk factor in motor vehicle crashes. To minimize the number of crashes and improve driving safety, it is necessary to investigate driver workload and the most influential factor that affects driver workload. In this study, we examined the effects of road geometry, driving performance (by both steering wheel and pedal controls), and secondary task modality type on driver workload. A hierarchical ordinary least squares multiple regression analysis revealed that visually demanding secondary task predicted higher driver workload. This finding could help in-vehicle interface designs to minimize driver workload and improve driving safety.

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