Modeled congestion probability for selected cities

Modeled congestion probability for selected cities based on Twitter and OpenStreetMap data based on grid cells in 100 meters resolution. The dataset includes the cities of Barcelona, Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ranges from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, machine learning was used to train a model that predicts the likelihood of congestion within cities. Publicly provided data from UBER were used as reference data (https://movement.uber.com). As indicators in the model, the number of tweets and the number of points of interest from OSM near roads were used. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.