# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 1 Introduction  
Nowadays, the excessive population growth in urban settings represents one of the biggest challenges for governments worldwide. The rapid growth of city populations directly affects the ability of countries to reduce contamination in order to improve the quality of life of their citizens. The urban population in 2014 accounted for 54% of the global population, up from 34% in 1960 and is expected to grow approximately 1.84% per year between 2015 and 2020, 1.63% per year between 2020 and 2025 and 1.44% per year between 2022 and 2030 [1]. Caring for the needs of a rapidly growing urban population is a major challenge for countries trying to create more sustainable cities. A clear example of the impact of population growth on the environment is contamination occurring in the atmosphere, which increases proportionally to the number of people living in cities and the number of motor vehicles in circulation [2]. During the last 50 years, human activity, specifically the burning of fossil fuels, has released an indiscriminate amount of CO\({}_{2}\) and other greenhouse gases, sufficient to retain more heat in the lower layers of the atmosphere thereby altering the global climate [3]. In addition to climatic change, air pollution also represents a major health hazard. According to a WHO report [4], air pollution in cities and rural areas throughout the world each year causes 3.7 millionpremature deaths, 80% of which are associated with diseases such as ischemic heart disease and stroke; 14% due to chronic obstructive pulmonary disease or acute infections of the lower respiratory tract and 6% to lung cancer. Globally, excessive air pollution is caused by unsustainable policies in sectors such as power generation, general industry and waste management, but mainly in the transportation sector [5]. Road vehicles are the major source of air pollution due to the use of fossil fuels, poor vehicle maintenance, old vehicles, poor fuel quality and the overuse of vehicles [6]. According to worldwide estimates published in 2014, there were a total of approximately 1.1 billion cars on the planet, which produced about 1,730,000,000 tons of CO\({}_{2}\), a figure that is expected to grow over the next few years since some estimates predict that by 2035 the number of cars will reach close 2 billion [7]. In recent years a study published in Environmental Science & Technology reveals that local spikes in atmospheric CO\({}_{2}\) concentrations may contribute to premature deaths by increasing ozone concentrations [8]. The excessive use of road vehicles not only harms both the environment but also affects the quality of life and the economy of the population. The phenomenon of traffic congestion is present in most developed cities. For example, according to the Dutch company Tom Tom [9], a worldwide company that produces navigation and mapping products using GPS technology and location-based products, the city of Istanbul, Turkey, is the world's most congested city at 58%, followed by Mexico City and Rio de Janeiro, with 55% and 51%, respectively. In Mexico City, for example, commuters can spend as much as 23 h per month in congested traffic, a situation that not only exposes them to high contamination levels, but contributes significantly to stress and obesity problems [10]. The loss of prolonged time commuting and working hours also has a strong impact on the productivity of businesses and the overall economy. Annually, the traffic-generated economic losses amounts to over 1.5 billion dollars, or 1% Mexico City's contribution to the country's GDP, a very significant figure for a developing country [11]. This article discusses the simulation and evaluation of a traffic congestion detection system by means of connected vehicles and big data that accurately identifies traffic congestion, so that drivers can change their routes thereby reducing potentially harmful emissions and travel time.