The traffic scenario in developing economies is fundamentally different from that of the developed economies. The latter is predominantly composed of passenger cars and can be aptly termed as ''homogeneous'' traffic, whereas the former is composed of vehicle types with a wide variety of static and dynamic characteristics, which occupy the same right of way, resulting in an unsynchronized movement of the vehicles. Another distinguishing characteristic of this traffic is the absence of lane-discipline, resulting from the wide variation in sizes and maneuvering abilities of the vehicles. These distinctions result in some phenomena like vehicle creeping, which are absent in the homogeneous traffic. Hence, this type of traffic can be referred to as ''heterogeneous disordered'' or ''mixed'' traffic. A review of the literature has shown that most of the studies in such traffic make use of the methods and concepts developed for homogeneous traffic. Very few studies have attempted to capture and understand the distinctive characteristics of the mixed traffic. The primary objective of this paper is to provide a review of the studies on various mixed traffic characteristics in developing economies, identify their limitations and provide guidelines for the future research. Also, a detailed methodology of the simulation process for the mixed traffic is given, reflecting the ''gap-filling'' rather than the conventional ''car-following'' behavior. A comparison of the past modeling approaches is also presented and the accuracy of their implementation is discussed.
<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>
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