Impact of Driving Behavior on the Traffic Safety of Highway Intersection

Xiaoqiu, Fan; Ji, Jinzhang; Zhang, Guoqiang

doi:10.1109/icmtma.2011.379

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…A study of driver behavior towards a large impact on traffic safety at a crossroads was previously conducted which studied the theme of traffic conflicts are carried out by adopting statistical methods to determine the weight of driver behavior [13]. This study shows that driving behavior that combines intersection characteristics is very helpful in explaining traffic performance at an intersection.…”

Section: B Development Of Underpasses As Civil Infrastructurementioning

confidence: 98%

Understanding Vertical Holiness as a Received Business on Underpass Development

Wibawa¹,

Redana²,

Suthanaya³

et al. 2020

IJEET

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Development efforts in the vertical direction should have been better than in the horizontal direction which only requires land to the side. The emergence of new ideas certainly raises the pros and cons, most local geniuses are vertical purity, the compilation passes under the terraced infrastructure there is a feeling of flare/fatigue because it is bypassed by people. The aim of the study is to reveal the vertical relationship with the design of underpasses, and how community acceptance of underpass construction. The methodology used is complicated through interviews with Hindu scholars, Bali, which are then used as a basis for further observation for shareholders so that questionnaires can be prepared. The results of the questionnaire are data of the relationship between vertical and attitude of community acceptance towards underpass development. The data obtained were analyzed by Logistic Regression. The results of the study of vertical simplicity are local wisdoms that need to be discussed and agreed in this modern era. Furthermore, the results of the analysis show that there is a relationship between vertical purity and uneven plot intersection height, as evidenced by the acceptance of the hypothesis at the significance level ? = 0.05. The results of the analysis show "significance <?" or (0.033 <0.05), this means vertical purity, which means significant effect on underpass height design. Alternative designs also need to reduce underpass differences. Invitations are made very necessary as a legal umbrella for the design and manufacture of underpasses. Keywords: Vertical purity, underpass, cemer/leteh, alternative design.

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Section: B Development Of Underpasses As Civil Infrastructurementioning

confidence: 98%

Understanding Vertical Holiness as a Received Business on Underpass Development

Wibawa¹,

Redana²,

Suthanaya³

et al. 2020

IJEET

View full text Add to dashboard Cite

show abstract

“…in various places in the world [2]. A survey on road safety has revealed that every year 1.3 million fatalities are recorded worldwide, as well as 50 million recorded injuries [3]. Road accidents have also been ranked as the leading cause of death among young people between 15 and 29 years of age [4].…”

Section: Introductionmentioning

confidence: 99%

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

et al. 2020

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Recent years have seen increasing utilization of deep learning methods to analyze large collections of medical data and signals effectively in the Internet of Medical Things (IoMT) environment.Application of these methods to medical signals and images can help caregivers form proper decisionmaking. One of the important IoMT medical application areas includes aggressive driving behaviors to mitigate road incidents and crashes. Various IoMT-enabled body sensors or camera sensors can be utilized for real-time monitoring and detection of drivers' bio-signal status such as heart rate, blood pressure, and drivers' behaviors. However, it requires a lightweight detection module and a powerful training module with realtime storing and analysis of drivers' behaviors data from these medical devices to detect driving behaviors and provides instant feedback by the administrator for safety, gas emissions, and energy/fuel consumption. Therefore, in this paper, we propose a bio-signal-based system for real-time detection of aggressive driving behaviors using a deep convolutional neural network (DCNN) model with edge and cloud technologies. More precisely, the system consists of three modules, which are the driving behaviors detection module implemented on edge devices in the vehicle, the training module implemented in the cloud platform, and the analyzing module placed in the monitoring environment connected with a telecommunication network. The DCNN model of the proposed system is evaluated using a holdout test set of 30% on two different processed bio-signal datasets. These two processed bio-signal datasets are generated from our collected bio-signal dataset by using two different time windows and two different time steps. The experimental results show that the proposed DCNN model achieves 73.02% of validation accuracy on the processed dataset 1 and 79.15% of validation accuracy on the processed dataset 2. The results confirm the appropriateness and applicability of the proposed deep learning model for detecting driving aggressive behaviors using bio-signal data.INDEX TERMS Internet of Medical Things (IoMT), bio-signal, aggressive driving behaviors, deep convolutional neural networks (DCNNs), cloud computing, Raspberry Pi.

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“…Besides the optimization of the car itself, there is also the potential to increase the energy-efficiency and improve road safety by adapting the individual driving behavior to the given driving situation. The studies (Xiaoqiu, Jinzhang, & Guoqiang, 2011) and (Chin & Quek, 1997) revealed that the driving behavior has an effect on road safety. This has also been verified in the accidents report of the German Statistical Office (German Statistical Office, 2014), which showed that 86% of the accidents with damage to persons in Germany in 2013 happened because of driver mistakes, see Fig.…”

Section: Introductionmentioning

confidence: 99%

Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system

Yay

Madrid

Ramírez

2016

Expert Systems with Applications

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An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energyefficiency or safety-relevant driving rules.

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Impact of Driving Behavior on the Traffic Safety of Highway Intersection

Cited by 5 publications

References 6 publications

Understanding Vertical Holiness as a Received Business on Underpass Development

Understanding Vertical Holiness as a Received Business on Underpass Development

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system

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