With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.
Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.
Data preparation is an essential stage in data analysis. Many institutions or companies are interested in converting data into pure forms that can be used for scientific and profit purposes. It helps you set goals regarding system capabilities and features or the benefits your company expects from its investment. This purpose creates an immediate need to review and prepare the data to clean the raw data. In this paper, we highlight the importance of data preparation in data analysis and data extraction techniques, in addition to an integrated overview of relevant recent studies dealing with mining methodology, data types diversity, user interaction, and data mining. Finally, we suggest some potential suggestions for future research and development.
The delivered effort in this manuscript is grounded on NS-2 (The Network Simulator 2) to implement the congestion control process of classic TCP (Transmission Control Protocol), with new congestion control mechanism. In this paper, a novel congestion control algorithm is offered, which contains of slow-start and congestion avoidance mechanisms. The proposed slow-start algorithm assumes a duplicating and an interpolating approach to the congestion window (cwnd) for each increment instead of the exponential increment used by other TCP source variants such as Reno, Vega, Tahoe, Newreno, Fack, and Sack. Furthermore, the enhanced congestion avoidance algorithm is built by using an improved Additive Increase Multiplicative Decrease (AIMD) algorithm with multi TCP flow facility, to provide an enhanced congestion control algorithm with some valuable properties to improve TCP routine for high speed protocols. The improvement strategy based on merging of slow start, congestion avoidance mechanism that are used in TCP congestion control, to create a new AIMD algorithm with a new relationship between the pair parameters a and b. This paper is also involved in the creation of rapid agent in NS-2 models designed to identify the modified TCP and to configure the NS-2 platform. A fast TCP also includes an innovative scheme to slow the rapid start to help TCP to start faster through the high speed networks and also to postpone the congestion state as much as possible.
The delivered effort in this manuscript is grounded on NS-2 (The Network Simulator 2) to implement the congestion control process of classic TCP (Transmission Control Protocol), with new congestion control mechanism. In this paper, a novel congestion control algorithm is offered, which contains of slow-start and congestion avoidance mechanisms. The proposed slow-start algorithm assumes a duplicating and an interpolating approach to the congestion window (cwnd) for each increment instead of the exponential increment used by other TCP source variants such as Reno, Vega, Tahoe, Newreno, Fack, and Sack. Furthermore, the enhanced congestion avoidance algorithm is built by using an improved Additive Increase Multiplicative Decrease (AIMD) algorithm with multi TCP flow facility, to provide an enhanced congestion control algorithm with some valuable properties to improve TCP routine for high speed protocols. The improvement strategy based on merging of slow start, congestion avoidance mechanism that are used in TCP congestion control, to create a new AIMD algorithm with a new relationship between the pair parameters a and b. This paper is also involved in the creation of rapid agent in NS-2 models designed to identify the modified TCP and to configure the NS-2 platform. A fast TCP also includes an innovative scheme to slow the rapid start to help TCP to start faster through the high speed networks and also to postpone the congestion state as much as possible.
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