scholarly journals Investigating the trip configured causal effect of distracted driving on aggressive driving behavior for e-hailing taxi drivers

2020 ◽  
Author(s):  
Muhammad Sajjad Ansar ◽  
Yongfeng Ma ◽  
Shuyan Chen ◽  
Kun Tang ◽  
Ziyu Zhang
Keyword(s):  
Causal Effect ◽  
Driving Behavior ◽  
Distracted Driving ◽  
Aggressive Driving ◽  
Taxi Drivers

scholarly journals Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

2020 ◽  
Vol 17 (11) ◽  
pp. 3937 ◽  
Author(s):  
Muhammad Zahid ◽  
Yangzhou Chen ◽  
Sikandar Khan ◽  
Arshad Jamal ◽  
Muhammad Ijaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Significant Proportion ◽  
Driving Behavior ◽  
Evaluation Metrics ◽  
Aggressive Driving ◽  
Taxi Drivers ◽  
Traffic Violations ◽  
The City ◽  
Stack Model

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

scholarly journals Use of multilevel modeling to examine variability of distracted driving behavior in naturalistic driving studies

2021 ◽  
Vol 152 ◽  
pp. 105986
Author(s):  
Sara A. Freed ◽  
Lesley A. Ross ◽  
Alyssa A. Gamaldo ◽  
Despina Stavrinos
Keyword(s):  
Multilevel Modeling ◽  
Driving Behavior ◽  
Distracted Driving ◽  
Naturalistic Driving

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

scholarly journals PERAN SELF CONTROL TERHADAP PERILAKU AGGRESSIVE DRIVING PENGGUNA SEPEDA MOTOR

Psycho Idea ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 79
Author(s):  
Muhammad Wika Kurniawan ◽  
Indra Prapto Nugroho
Keyword(s):  
Young Male ◽  
Sampling Technique ◽  
Driving Behavior ◽  
Self Control ◽  
Aggressive Driving ◽  
Control Scale ◽  
Behavior Scale ◽  
Trial Participants ◽  
Simple Regression

The aim of the study is determining whether there is a role of self control toward aggressive driving behavior on motorcyclist. This study hypothesizes that there is a role of self control toward aggressive driving behavior on motorcyclist. This study used 200 young male motorcyclists in South Sumatera as participants who already has driving license C and used 50 motorcyclists as the trial participants. The sampling technique was purposive sampling. The study measurements are self control scale and aggressive driving behavior scale that refer to Averill’s (1973) self control types and Tasca’s (2000) aggressive driving behavior forms. Data analysis used simple regression.The result of simple regression shows R square = 0,507, F= 203,680, and p = 0,000 (p<0,05). This means that self control has a significant role toward aggressive driving behavior. Thus, the hypothesis could be accepted and self control contribution toward aggressive driving behavior is 50,7%.

scholarly journals INDIVIDUAL VALUE SYSTEM CHARACTERISTICS AND DRIVING BEHAVIOR RELATIONSHIPS

2017 ◽  
Vol 1 ◽  
pp. 570
Author(s):  
Krists Jānis Lazdiņš ◽  
Kristīne Mārtinsone
Keyword(s):  
Driving Behavior ◽  
Distracted Driving ◽  
Value System ◽  
System Integrity ◽  
Dangerous Driving ◽  
The Future ◽  
And Gender ◽  
The Individual ◽  
Individual Value ◽  
System Characteristics

The aim of research was to examine characteristics of individual value system prediction for driving behavior. It raised fundamental question for the research: 1. which of the individual value system characteristics predict driving behavior controlling gender and age. In the study participated 108 respondents, 40 (37.0%) men and 68 (63.0%) women who filled the questionnaire on the internet. There was used two questionnaires – „Latvian driving behavior survey”, The value and levels of availability relations in different spheres of life” The results showed that the value system integrity / disintegrity indicator predicts distracted driving, explains 18% of variation and is statistically significantly. Internal vacuum and age statistically significantly negatively predicts risky driving explaining 17% of variation. Age statistically significantly predicts safe and courteous driving, explains 12% of variation. Value system integrity / disintegrity indicator and gender, statistically significantly negatively predicts summary indicator of dangerous driving, explains 22% of variation. Age statistically significantly negatively predicts distracted driving, explains 30% of variation. Limitations of the research are related to the size of the sample, alignment of participants and use of new instruments, as well as data collection method. If the study would be repeated in the future, it would be desirable to increase the sample size and use approbated instrument. It would be interesting to find out how the value of individual factors predicts objective size of accidents and violations caused by driving. The results can serve as the basis to create new driving behavior interventions and also applicable to psychologist's professional work, when counseling individuals of this group, as well as can be used in the future development of the field, science and research.

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