The metro may be considered to be the structural backbone of most cities that have it. The frequency and patterns of its usage indirectly affect the various social and economic processes.
Here in the lab, we model the passenger flows based on the influence of factors that determine the intra-urban mobility of city dwellers and the ridership rates at different groups of stations at different times of the day. To this end, a probabilistic model based on the biorthogonal decomposition is used to take into account the daily, weekly and seasonal variation in transport usage.
Due to the fact that retail and other types of enterprise benefit from the accessibility of the metro, the reproduction of pedestrian flows can be used to estimate the spatial distribution of cash flows associated with the daily use of venues and services in the city (commercial institutions, shops, catering facilities). Each of these objects establishes working hours and has a range of potential consumers or visitors, which links the consumer activity in areas surrounding metro stations to the volume of relevant passenger flows.
As an illustration of this concept, above is a simulation of the daily dynamics of passenger ridership rates at different metro stations in Saint Petersburg, coupled with a model-generated consumer activity hotspot heatmap.
Evaluation of urban mobility using surveillance cameras // Procedia Computer Science. — 2015. — Vol. 66. — pp. 364-372.