# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 2 Background
### Congestion Problem and Impact on Society  
Many traffic experts and companies worldwide deal with congestion issues and try to find various solutions to mitigate this problem. Congestion is a big issue in most cities around the world and significantly affects citizen mobility. For example, in 2019, according to INRIX Research, the top five most congested cities were (1) Moscow, (2) Istanbul, (3) Bogota, (4) Mexico City, (5) Sao Paulo [6], where three of five cities overlap with HERE's ranking of the top 5 most congested cities [7]. INRIX Research calculated the congestion impact rank based on a city's population and the delay attributable to congestion. Ranking by hours lost in congestion shows that 8 of the top 10 congested cities globally are in Europe. The reason for this probably lies in old downtown areas with narrow streets, which can be traced back to the Roman period [6], with a small capacity. According to TomTom's ranking, the top five most congested cities worldwide are (1) Mexico City, (2) Bangkok, (3) Jakarta, (4) Chongqing, (5) Bucharest. They calculated the congestion level as an increase in overall travel times compared to the free flow or uncongested situation. By analyzing the top five cities, citizens spend approximately 50% more time traveling in congestion conditions than in free flow conditions [8]. Travel speeds, congestion level, and time-loss positively correlate with population and density of population in a city [6]. However, the authors in [9] warn that the INRIX and TomTom congestion indices can be pessimistic. Their congestion indices are based on the speed data collected by their subscribers, who tend to drive in congestion more than average, and so exaggerate the congestion level for the average driver. Obviously, large cities usually create larger GDP and have possibilities of developing adequate infrastructure. The authors in [10] concluded that cities with higher GDP generally build more infrastructure, which helps in reducing overall congestion. Traffic congestion is not just a problem for commuters, it also has an impact on the economy [11] and the environment [12]. [PERSON] et al. [13] established a framework for traffic-related evaluation of air pollution using mobile crowd-sourced data, such as cellular network data and Global Navigation Satellite System (GNSS) data. Economic impact, combining direct costs, like delay in travel times, fuel wasted, and indirect ones, such as pollution by generated emissions, reduction in life satisfaction and driver stress [9]. INRIX Research estimates congestion costs in the USA, UK, and Germany for the top 10 most congested cities to be around USD 46B generated in the USA, GBP 6.8B in the UK, and EUR 5.2B in Germany [6]. The congestion costs depend upon the labor market, industrial sector, mode of transport, trip distance, and travel conditions. To assess these costs, public sector managers need accurate operational models. [PERSON] and [PERSON] [14] used stochastic queueing models for congestion costs calculation. [PERSON] et al. [15], used three outgoings for estimating congestion cost: opportunity costs, vehicle operating costs, and fuel consumption quantity. Opportunity costs use delay, number of vehicles, average vehicle occupancy for a specific mode of transport, and value of time for a specific mode of transport estimated using a socio-economic survey. Vehicle operating cost consists of fuel cost for a specific mode of transport and delay. Consumption quantity includes fuel price of specific fuel types (Gasoline, Diesel, Compressed natural gas). In [16] authors analyzed traffic congestion costs related to the road transport of passengers. They presented an analysis of two cost factors of road traffic congestion: productivity time loss and fuel energy consumption. Results indicate that personalized transport significantly contributes to traffic congestion and imposes significant losses to the economy.