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

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          Abstract

          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.

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          Stacked generalization

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            Driving speed and the risk of road crashes: a review.

            Driving speed is an important factor in road safety. Speed not only affects the severity of a crash, but is also related to the risk of being involved in a crash. This paper discusses the most important empirical studies into speed and crash rate with an emphasis on the more recent studies. The majority of these studies looked at absolute speed, either at individual vehicle level or at road section level. Respectively, they found evidence for an exponential function and a power function between speed and crash rate. Both types of studies found evidence that crash rate increases faster with an increase in speed on minor roads than on major roads. At a more detailed level, lane width, junction density, and traffic flow were found to interact with the speed-crash rate relation. Other studies looked at speed dispersion and found evidence that this is also an important factor in determining crash rate. Larger differences in speed between vehicles are related to a higher crash rate. Without exception, a vehicle that moved (much) faster than other traffic around it, had a higher crash rate. With regard to the rate of a (much) slower moving vehicle, the evidence is inconclusive.
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              Orange: data mining toolbox in Python

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                02 June 2020
                June 2020
                : 17
                : 11
                : 3937
                Affiliations
                [1 ]College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; zahid@ 123456emails.bjut.edu.cn
                [2 ]College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China; yzchen@ 123456bjut.edu.cn
                [3 ]Department of Mechanical Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5069, Dhahran 31261, Saudi Arabia
                [4 ]Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5055, Dhahran 31261, Saudi Arabia; arshad.jamal@ 123456kfupm.edu.sa
                [5 ]School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; m.ijaz58@ 123456yahoo.com
                [6 ]UHasselt, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium; tufail.ahmed@ 123456uhasselt.be
                Author notes
                Author information
                https://orcid.org/0000-0002-9143-8328
                https://orcid.org/0000-0001-7284-7348
                Article
                ijerph-17-03937
                10.3390/ijerph17113937
                7312618
                32498347
                9c0cc5b4-fdee-4f83-957f-e52c30d63d9f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 May 2020
                : 30 May 2020
                Categories
                Article

                Public health
                aggressive driving,traffic violations,hotspot analysis,geographic information system (gis),machine learning,taxi drivers

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