Urban traffic modeling and pattern detection using online map vendors and self-organizing maps

Typical traffic modeling approaches,such as network-based methods and simulation models,have been shown inadequate for urban-scale studies due to the fidelity issue of models.As a go-around,data-driven models have received increasing attention recently.However,most data-driven methods have been restricted by their data source and cannot be scaled up to manage urban-and regional-scale studies.Regarding this issue,this research proposes a pipeline that collects traffic data from online map vendors to bypass data limitations for large-scale studies.The study consists of two experiments:1)recognizing the dominant traffic patterns of cities and 2)site-specific predictions of typical traffic or the most probable locations of patterns of interests.The experiments were conducted on 32 Swiss cities using traffic data that were collected for a two-month period.The results show that dominant patterns can be extracted from the temporal traffic data,and similar patterns exist not only in various parts of a city but also in different cities.Moreover,the results reveal that a country-level lockdown decreased traffic congestions in regional highways but increased those connections near the city centers and the country borders.