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

Abstract

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.

Key words： Urban traffic patterns; Data-driven modeling; Urban management; Map vendors