[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122156-en":3,"doc-seo-122156-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122156,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Analysis of Informal Minibus Taxi Driving","This paper investigates driving behaviors of informal minibus taxis in both controlled and uncontrolled conditions, where safety and driving-pattern knowledge remain limited. Using exploratory factor analysis on smartphone GPS data collected from a passenger’s device, the study extracts key driving behaviors and recurring patterns. Results reveal meaningful differences in driving styles between controlled and uncontrolled environments. The insights support safety improvement efforts for policymakers, transportation planners, and technology developers in informal urban transport.","Machine Learning Analysis of Informal Minibus Taxi Driving  \nNomfundo Cele1 , ∗ , Alain Kibangou2,3 , ∗∗ , and Walter Musakwa3 , ∗∗∗  \n1University of Johannesburg, Department of Urban and Regional Planning, Doornfontein Campus,0184 Johannesburg, South Africa.  \n2Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, Gipsa-Lab, F-38402 Saint Martin d’Hères, France.  \n3University of Johannesburg, Faculty of Science, GEMES, Auckland park Campus, 2006 Johannesburg, South Africa  \nAbstract. This paper presents a machine learning analysis of driving behaviors in informal minibus taxis, focusing on both controlled and uncontrolled environments. Informal minibus taxis play a crucial role in urban transportation, particularly in developing countries, yet their driving patterns and safety implications remain under-explored. We utilize exploratory factor analysis to analyze data collected from smartphone GPS carried by a passenger of a minibus taxi, identifying key driving behaviors and patterns. Our study highlights significant differences in driving styles between controlled and uncontrolled environments, offering insights into safety and efficiency. The findings provide valuable information for policymakers, transportation planners, and technology developers aiming to enhance urban mobility and safety in the informal transport sector.  \n1 Introduction  \nWhile there is growing interest in the health benefits of mobility (reduction of sedentary lifestyles, active mobility), we must not forget that mobility is also a source of pollution and accidents. According to the World Health Organization (WHO), each year road accidents kill over 1.35 million people. An increased burden of road accident deaths and injuries happens in low- and middle-income countries, and the problem is getting worse as a result of rapid motorisation and urbanisation [1] . In most of the developing world, transportation services are provided by independent stakeholders – the private sector – and constitute a greater share of road accident fatalities and injuries, which in turn account for an increasing and large amount of disease burden and death [2] . In South Africa, the road traffic accident mortality rate is 27 fatalities for a 100,000 population, which is over double the international average and well over the African region average. In South Africa, 75% of households depend on public transport, particularly with minibus taxis [3] . However, it is more regularly involved in road traffic accidents compared to other modes. Indeed, minibus taxis account for 70, 000 road traffic accidents each year, the double of the number of other public transport modes [4] . Pedestrian injury-related deaths account for 38% of all traffic deaths, followed by driver and passenger deaths at 26% and 33%[5] .  \nRoad safety and road accidents have become a serious public health issue, leading The United Nations to incorporate road safety into the 17 Sustainable development goals (SDGs), in particular, SDG 3 on good health and well-being and SDG 11 on sustainable cities and com-  \n∗ e-mail: [nomfundocele@gmail.com](nomfundocele@gmail.com)  \n∗∗ e-mail: [alain.kibangou@univ-grenoble-alpes.fr](alain.kibangou@univ-grenoble-alpes.fr)[ ](alain.kibangou@univ-grenoble-alpes.fr)∗∗∗[e-mail: wmusakwa@uj.ac.za](e-mail: wmusakwa@uj.ac.za)  \nmunities [6] . The persistence and extent of this public health issue demands new methods for road safety procedures [7] . Because of the second Decade of Action for Road Safety, announced by the World Health Organization as the 2021-2030 Decade of Action for Road Safety, road safety has become a major area of interest for researchers, policymakers, and transport engineers [8] . Traditional methods to improve road safety are centred on executing seminars and workshops on safe driving, awareness campaigns on safe driving, and implementing strict road rules to change the behavior and attitude of road users [9] .  \nResearch conducted by public institutions, private entiti","cbCaioJHx4XQr5IO","https://ap.wps.com/l/cbCaioJHx4XQr5IO","pdf",1593589,1,6,"English","en",105,"# Introduction\n## Road safety as a public health challenge\n## Human error and contributing factors to accidents\n## Driving style concepts and prior classifications","[{\"question\":\"What driving environments does the study compare for informal minibus taxis?\",\"answer\":\"The study focuses on both controlled and uncontrolled environments to examine how driving behaviors differ across contexts.\"},{\"question\":\"How is the driving data collected and analyzed?\",\"answer\":\"Smartphone GPS data carried by a passenger is analyzed using exploratory factor analysis to identify key driving behaviors and patterns.\"},{\"question\":\"Why are informal minibus taxis important for this research?\",\"answer\":\"They play a crucial role in urban transportation in developing countries, yet their driving patterns and safety implications have been under-explored.\"}]","Machine Learning Analysis of Informal Minibus Taxi Driving | 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