J0120101 Traffic Accident Simulation and Evaluation of Driving Safety Support System : Utilization and Management of Traffic Accident Simulation - 1

2014 ◽  
Vol 2014 (0) ◽  
pp. _J0120101--_J0120101-
Author(s):  
Hideki FUJII ◽  
Shinobu YOSHIMURA
Keyword(s):  
Support System ◽  
Traffic Accident ◽  
Driving Safety ◽  
System Utilization ◽  
Accident Simulation ◽  
Simulation And Evaluation

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Computerized clinical decision support system utilization in nursing

2017 ◽  
Vol 15 (11) ◽  
pp. 2638-2644
Author(s):  
Dóris Ribeiro Ortiz ◽  
Flávia de Oliveira Motta Maia ◽  
Diley Cardoso Franco Ortiz ◽  
Heloísa Helena Ciqueto Peres ◽  
Paulino Artur Ferreira de Sousa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
System Utilization

scholarly journals A spatial decision support system for traffic accident prevention in different weather conditions

2021 ◽  
Vol 61 (1) ◽  
Author(s):  
Danijel Ivajnšič ◽  
David Pintarič ◽  
Veno Jaša Grujić ◽  
Igor Žiberna
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Traffic Accident ◽  
Road Traffic ◽  
Weather Conditions ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Road Traffic Safety ◽  
Road Segments

Natural conditions play an important role as determinants and cocreators of the spatiotemporal road traffic accident Hot Spot footprint; however, none of the modern commercial, or open-source, navigation systems currently provides it for the driver. Our findings, based on a spatiotemporal database recording 11 years of traffic accidents in Slovenia, proved that different weather conditions yield distinct spatial patterns of dangerous road segments. All potentially dangerous road segments were identified and incorporated into a mobile spatial decision support system (SLOCrashInfo), which raises awareness among drivers who are entering or leaving the predefined danger zones on the street network. It is expected that such systems could potentially increase road traffic safety in the future.

scholarly journals A Study on the Differences in Driving Skills of Chinese Bus and Taxi Drivers

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Zuobo Zhang ◽  
Xuxin Zhang ◽  
Nuoya Ji ◽  
Shanshan Lin ◽  
Kun Wang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Road Traffic ◽  
Driving Safety ◽  
Bus Drivers ◽  
Taxi Drivers ◽  
Public Transport System ◽  
Road Traffic Safety ◽  
Driving Skills ◽  
Urban Public Transport

Professional drivers constitute an important group of drivers who shoulder the responsibility of safely transporting passengers and cargo. Bus drivers and taxi drivers are an important part of the urban public transport system, and their driving safety affects road traffic safety. Therefore, the purpose of this paper is to explore the differences between bus drivers and taxi drivers in their driving behaviors and driving skills and to predict their traffic accident involvement based on these behaviors and skills. We conducted a field survey of 274 bus drivers and 178 taxi drivers in Hefei, China. The results revealed significant differences between bus drivers and taxi drivers in terms of violations, lack of concentration and technical driving skills. Aggression and violations had significant predictive effects on bus drivers’ traffic accident involvement, and memory lapses and a lack of safety consciousness had significant predictive effects on taxi drivers’ accident involvement.

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