scholarly journals Estimation of Crash Severity on Mountainous Freeways in Chongqing

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yunwei Meng
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Crash Severity ◽  
Driving Experience ◽  
Factors Affecting ◽  
The Past ◽  
Accident Severity ◽  
Partial Proportional Odds Model ◽  
The Times

Mountainous freeways always suffer from accidents due to special terrain, weather conditions, driving environment, and so on. Based on the records of 898 accidents that occurred on mountainous freeways in Chongqing during the past 6 years, the partial proportional odds model is used to identify the factors affecting the accident severity. The time of the accident, season, involvement of trucks, accident characteristics, speeding, maximum driving experience of involved drivers, and weather and road conditions are found to be important for the levels of accident severity. Zero to 6 a.m. and 19 to 24 p.m. are the times prone to serious traffic accidents. The probability of serious traffic accidents in summer and autumn is greater than that in spring and winter. Once a truck is involved in an accident, the consequence is often more severe. Turnover and speeding will result in a grave accident. When there is an experienced driver, the probability of serious traffic accidents is low. The fog is extremely unfavorable weather conditions. The probability of serious accident happening in the downgrade, ramp, curve, bridge, and tunnel sections is greater than the others. The results aim to provide valuable reference for traffic safety on mountainous freeways.

scholarly journals Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach

2021 ◽  
Vol 18 (23) ◽  
pp. 12725
Author(s):  
Shuaiming Chen ◽  
Haipeng Shao ◽  
Ximing Ji
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Light Condition ◽  
Combined Effect ◽  
Special Focus ◽  
Novice Drivers ◽  
Fatal Accident ◽  
Driving Experience ◽  
Factors Affecting ◽  
Accident Severity

Traffic accidents have significant financial and social impacts. Reducing the losses caused by traffic accidents has always been one of the most important issues. This paper presents an effort to investigate the factors affecting the accident severity of drivers with different driving experience. Special focus was placed on the combined effect of driving experience and age. Based on our dataset (traffic accidents that occurred between 2005 and 2021 in Shaanxi, China), CatBoost model was applied to deal with categorical feature, and SHAP (Shapley Additive exPlanations) model was used to interpret the output. Results show that accident cause, age, visibility, light condition, season, road alignment, and terrain are the key factors affecting accident severity for both novice and experienced drivers. Age has the opposite impact on fatal accident for novice and experienced drivers. Novice drivers younger than 30 or older than 55 are prone to suffer fatal accident, but for experienced drivers, the risk of fatal accident decreases when they are young and increases when they are old. These findings fill the research gap of the combined effect of driving experience and age on accident severity. Meanwhile, it can provide useful insights for practitioners to improve traffic safety for novice and experienced drivers.

scholarly journals Study and Simulation Analysis of Vehicle Rear-End Collision Model considering Driver Types

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Luo ◽  
Xinqiang Chen ◽  
Jie Yuan ◽  
Xiaodong Zang ◽  
Junheng Yang ◽  
...  
Keyword(s):  
Reaction Time ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Simulation Analysis ◽  
Fuzzy Theory ◽  
Driving Safety ◽  
Driving Experience ◽  
Factors Affecting ◽  
Safety Distance ◽  
Traffic Regulation

The reasonable distance between adjacent cars is very crucial for roadway traffic safety. For different types of drivers or different driving environments, the required safety distance is different. However, most of the existing rear-end collision models do not fully consider the subjective factor such as the driver. Firstly, the factors affecting driving drivers’ characteristics, such as driver age, gender, and driving experience are analyzed. Then, on the basis of this, drivers are classified according to reaction time. Secondly, three main factors affecting driving safety are analyzed by using fuzzy theory, and the new calculation method of the reaction time is obtained. Finally, the improved car-following safety model is established based on different reaction time. The experimental results have shown that our proposed model obtained more accurate vehicle safety distance with varied traffic kinematic conditions (i.e., different traffic states, varied driver types, etc.). The findings can help traffic regulation departments issue early warnings to avoid potential traffic accidents on roads.

scholarly journals What Affects the E-Bicycle Speed Perception in the Era of Eco-Sustainable Mobility: A Driving Simulator Study

2021 ◽  
Vol 13 (9) ◽  
pp. 5252
Author(s):  
Sreten Simović ◽  
Tijana Ivanišević ◽  
Aleksandar Trifunović ◽  
Svetlana Čičević ◽  
Dragan Taranović
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Sustainable Mobility ◽  
Driving Experience ◽  
Factors Affecting ◽  
Advantages And Disadvantages ◽  
Speed Perception ◽  
Simulator Study ◽  
H 20

The increase in the number of electric bicycles worldwide has resulted in a rise in the number of traffic accidents involving e-bicyclists. Previous studies have been based on analyzing the use, advantages and disadvantages of e-bicycles, whereas only a small number of studies have been focused on analyzing the e-bicycle traffic safety, particularly the factors leading to the occurrence of traffic accidents. One of the factors affecting the occurrence of traffic accidents is the incorrect perception of the e-bicycle speed by other traffic participants. To examine the mentioned problem, the authors of this paper conducted an experimental study to determine what affects the e-bicycle speed perception. The experiment included 175 participants, aged 18 to 50. The research was conducted under laboratory conditions using a driving simulator, at different e-bicycle speeds (10 km/h, 20 km/h and 30 km/h), in the situations in which the e-bicyclist was (not) using a reflective vest. The results show statistically significant differences in the e-bicycle speed perception when the e-bicyclist does not use/uses a reflective vest. Besides, the driving licence categories of traffic participants and their driving experience also have a significant impact on the perception of the e-bicycle speed.

scholarly journals The Effects of Picking Up Primary School Pupils on Surrounding Street’s Traffic: A Case Study in Hanoi

2020 ◽  
Vol 14 (1) ◽  
pp. 237-250
Author(s):  
Dinh Hiep ◽  
Vu V. Huy ◽  
Teppei Kato ◽  
Aya Kojima ◽  
Hisashi Kubota
Keyword(s):  
Primary School ◽  
Traffic Safety ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Primary Schools ◽  
Traffic Accidents ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Almost All

Introduction: One of the significant characteristics of schools in Vietnam is that almost all parents send their children to school and/or pick up their children from school using private vehicles (motorcycles). The parents usually stop and park their vehicle on streets outside the school gates, which can lead to serious congestion and increases the likelihood of traffic accidents. Methods: The objective of this study is to find out factors affecting the picking up of pupils at primary school by evaluating the typical primary schools in Hanoi city. A binary logistic regression model was used to determine factors that influence the decision of picking up pupils and the waiting duration of parents. The behavior of motorcyclists during the process of picking up pupils at the primary school gate has been identified and analyzed in detail by the Kinovea software. Results and Discussion: The study showed that, on the way back home, almost all parents use motorbikes (89.15%) to pick up their children. During their waiting time (8.48 minutes in average), they made a lot of illegal parking actions on the street there by, causing a lot of “cognitive” errors and “crash” points surrounding in front of the primary school entrance gate. Risky picking-up behaviors were significantly observed, i.e. picking-up on opposite side of the school, making a U-turn, backing-up dangerously, parking on the middle of street, and parking on the street next to sidewalk). Conclusion: Based on the analyzed results, several traffic management measures have been suggested to enhance traffic safety and reduce traffic congestion in front of school gates. In addition, the results of the study will provide a useful reference for policymakers and authorities.

Impending Collision Judgment from an Egocentric Perspective in Real and Virtual Environments: A Review

Perception ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 769-795 ◽  
Author(s):  
Ilja T. Feldstein
Keyword(s):  
Virtual Environments ◽  
Observation Time ◽  
Field Of View ◽  
Spatial Distance ◽  
Driving Experience ◽  
Factors Affecting ◽  
The Past ◽  
Technical Factors ◽  
Potential Factors ◽  
Experimental Findings

The human egocentric perception of approaching objects and the related perceptual processes have been of interest to researchers for several decades. This article gives a literature review on numerous studies that investigated the phenomenon when an object approaches an observer (or the other way around) with the goal to single out factors that influence the perceptual process. A taxonomy of metrics is followed by a breakdown of different experimental measurement methods. Thereinafter, potential factors affecting the judgment of approaching objects are compiled and debated while divided into human factors (e.g., gender, age, and driving experience), compositional factors (e.g., approaching velocity, spatial distance, and observation time), and technical factors (e.g., field of view, stereoscopy, and display contrast). Experimental findings are collated, juxtaposed, and critically discussed. With virtual-reality devices having taken a tremendous developmental leap forward in the past few years, they have been able to gain ground in experimental research. Therefore, special attention in this article is also given to the perception of approaching objects in virtual environments and put in contrast to the perception in reality.

scholarly journals Temporal processing of past and future autobiographical events in patients with schizophrenia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hédi Ben Malek ◽  
Arnaud D’Argembeau ◽  
Mélissa C. Allé ◽  
Nicolas Meyer ◽  
Jean-Marie Danion ◽  
...  
Keyword(s):  
Temporal Processing ◽  
Temporal Order ◽  
Weather Conditions ◽  
Temporal Information ◽  
Direct Access ◽  
Think Aloud ◽  
Event Representation ◽  
The Past ◽  
Future Events ◽  
The Times

Abstract People with schizophrenia experience difficulties in remembering their past and envisioning their future. However, while alterations of event representation are well documented, little is known about how personal events are located and ordered in time. Using a think-aloud procedure, we investigated which strategies are used to determine the times of past and future events in 30 patients with schizophrenia and 30 control participants. We found that the direct access to temporal information of important events was preserved in patients with schizophrenia. However, when events were not directly located in time, patients less frequently used a combination of strategies and partly relied on different strategies to reconstruct or infer the times of past and future events. In particular, they used temporal landmark events and contextual details (e.g., about places, persons, or weather conditions) less frequently than controls to locate events in time. Furthermore, patients made more errors when they were asked to determine the temporal order of the past and future events that had been previously dated. Together, these findings shed new light on the mechanisms involved in locating and ordering personal events in past and future times and their alteration in schizophrenia.

scholarly journals Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

2020 ◽  
Vol 10 (5) ◽  
pp. 1675 ◽  
Author(s):  
Ciyun Lin ◽  
Dayong Wu ◽  
Hongchao Liu ◽  
Xueting Xia ◽  
Nischal Bhattarai
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Rural Areas ◽  
Computational Cost ◽  
The United States ◽  
Crash Severity ◽  
Rural Roads ◽  
Factors Affecting ◽  
West Texas ◽  
Teen Drivers

Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

scholarly journals HOW USUAL BEHAVIOUR CAN AFFECT PERCEIVED DRIVERS’ PSYCHOLOGICAL STATE WHILE DRIVING

Transport ◽  
2015 ◽  
Vol 32 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Angelo Stephen Cardamone ◽  
Laura Eboli ◽  
Carmen Forciniti ◽  
Gabriella Mazzulla
Keyword(s):  
Human Factors ◽  
Sample Survey ◽  
Psychological State ◽  
Road Accidents ◽  
Key Factors ◽  
Model Formulation ◽  
Driving Experience ◽  
Factors Affecting ◽  
Driving Behaviour ◽  
Accident Severity

Road accidents have a relevant impact in terms of economic and social costs. As a consequence, many research studies have focused on identifying the key factors affecting accident severity. Traditionally, these factors can be included in the infrastructural, human and vehicle groups. Among these, human factors have a relevant impact on accident severity, which depends on driving experience, driver’s socio-economic characteristics, and driving behaviour, but also on the driver’s psychological state while driving. In this paper we investigate on the relationships between driving behaviour usually taken by the driver and his/her perceived psychological state while driving. In order to achieve this goal we adopt an Ordered Probit (OP) model formulation calibrated on the basis of experimental data collected by a sample survey. We demonstrate that the adopted methodology accounts for the differential impacts of certain human factors on driver’s psychological state.

scholarly journals Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

2019 ◽  
Vol 16 (9) ◽  
pp. 1542 ◽  
Author(s):  
Xinhua Mao ◽  
Changwei Yuan ◽  
Jiahua Gan ◽  
Shiqing Zhang
Keyword(s):  
Risk Factors ◽  
Traffic Accidents ◽  
Multinomial Logistic Regression ◽  
Weather Condition ◽  
Weather Conditions ◽  
Traffic Density ◽  
Vehicle Speed ◽  
Factors Affecting ◽  
Traffic Characteristics ◽  
Rainy Weather

As a critical configuration of interchanges, the weaving section is inclined to be involved in more traffic accidents, which may bring about severe casualties. To identify the factors associated with traffic accidents at the weaving section, we employed the multinomial logistic regression approach to identify the correlation between six categories of risk factors (drivers’ attributes, weather conditions, traffic characteristics, driving behavior, vehicle types and temporal-spatial distribution) and four types of traffic accidents (rear-end, side wipe, collision with fixtures and rollover) based on 768 accident samples of an observed weaving section from 2016 to 2018. The modeling results show that drivers’ gender and age, weather condition, traffic density, weaving ratio, vehicle speed, lane change behavior, private cars, season, time period, day of week and accident location are important factors affecting traffic accidents at the weaving section, but they have different contributions to the four traffic accident types. The results also show that traffic density of ≥31 vehicle/100 m has the highest risk of causing rear-end accidents, weaving ration of ≥41% has the highest possibility to bring about a side wipe incident, collision with fixtures is the most likely to happen in snowy weather, and rollover is the most likely incident to occur in rainy weather.

scholarly journals Analysis of driver behavior in Amman using Manchester Driver Behavior Questionnaire

2021 ◽  
Author(s):  
Khair Jadaan ◽  
Noor Albeetar ◽  
Dania Abuhalimeh ◽  
Yara Naji
Keyword(s):  
Social Sciences ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Driver Behavior ◽  
Causative Factor ◽  
Background Information ◽  
Risky Driving ◽  
Driving Experience ◽  
Spss Software ◽  
The Relationship

A key component in combating traffic accidents is to study the contributory factors behind them, among these factors, the driver behavior stands out as the main causative factor. One of the most effective tools used worldwide in measuring self-reported driving components is the Manchester Driver Behavior Questionnaire (DBQ), it investigates the relationship between the driver and accidents involvement, throughout the analysis of both sociodemographic characteristics of drivers, and the risky driving components practiced such as; violations, errors and lapses. The present study investigates the factor structure of the DBQ and examines the relationships between the driver behavior factors and accident involvement. A survey questionnaire including the DBQ and background information was filled by a randomly selected sample of drivers in Amman, the capital of Jordan and the Statistical Package for Social Sciences (SPSS) software was used for data analysis. Driver behavior differed according to the gender, educational level and driving experience of the respondents. The results reflected the lifestyle, way of thinking and the general attitude of the driver and its relationship with traffic safety.

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