Abstract
Traffic congestion affects quality of life by inducing frustration and wasting time. The congestion is also critical to vehicles with high emergencies such as ambulances or police cars. This leads to additional CO2 emissions. Traffic management requires the accurate modeling of congestion levels. Two main observable parameters identify the congestion state of a city: vehicle speed and density. Congestion has an intuitive definition rather than a quantitative one, and is associated with the disorder and randomness occurring in traffic parameters. Therefore, statistical analysis offers an efficient and natural framework for modeling such disorders. In this study, a differential-entropy-based approach was proposed for labelling purposes. Subsequently, supervised congestion prediction from traffic meta-parameters based on a convolutional neural network was proposed. Traffic parameters includes node localization, date, day of the week, time of day, special road conditions, and holidays. The proposed model is validated on the CityPulse dataset, which is a set of vehicle traffic records, collected in Aarhus city in Denmark over a period of six months, for 449 observation nodes. Simulation results on the CityPulse dataset illustrate that the proposed approach yields accurate prediction rates for different nodes considered. The proposed system can prevent traffic congestion by reorienting the drivers to follow other itineraries.
Highlights
R ECENT years, population explosion, and the increase in vehicles as a transport mode have caused a tremendous increase in the number of vehicles, leading to problems in traffic such as congestion, environmental pollution, noise, and mobility latency
The datasets are publicly available in comma-separated values (CSV) raw format and the semantically interpreted format provided in the framework of the CityPulse EU FP7 project
In this study, we have tackled the problem of traffic congestion prediction based on meta-parameters
Summary
R ECENT years, population explosion, and the increase in vehicles as a transport mode have caused a tremendous increase in the number of vehicles, leading to problems in traffic such as congestion, environmental pollution, noise, and mobility latency. Congestion problems can cause traffic non safety under certain circumstances and event occurrences. Such problems affect the quality of life of citizens and can be penalized for the economy. In Brazil, congestion causes economic losses, estimated at 80 $ billion (BRL) per year. In Europe, such loss is about 2 % of its domestic product (GDP), and in USA, it is about 160 billion $ [1], while the infrastructure issue for congestion mitigation is far from ensuring an optimal use of resources, it can be very expensive and requires time to be ready. Traffic management system (TMS)-based solutions can raise such challenges by optimal management of existing resources
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