Optimizing transport frequency in multi-layered urban transportation networks for pandemic prevention

In this paper, we show how transport policy decisions regarding vehicle scheduling frequency can affect the pandemic dynamics in urban populations. Specifically, we develop a multi-agent simulation framework to model infection dynamics in complex transportation networks. Our agents periodically commute between home and work via a combination of walking routes and public transit, and make decisions intelligently based upon their location, available routes, and expectations of public transport arrival times. Our infection scheme allows for different levels of contagiousness, as a function of where the agents interact (i.e., inside or outside). The results show that the pandemic’s scale is heavily impacted by the network’s structure, and the decision making of the agents. In particular, the progression of the pandemic greatly differs when agents primarily infect each other in a crowded urban transportation system, opposed to while walking. We also assess the effect of modifying the public transport’s running frequency on the virus spread. Lowering the running frequency can discourage agents from taking public transportation too often, especially for shorter distances. On the other hand, the low frequency contributes to more crowded streetcars or subway cars if the policy is not designed correctly, which is why such an analysis may prove valuable for finding “sweet spots” that optimize the system. The proposed approach has been validated on real-world data, and a model of the transportation network of downtown Toronto, Canada. The used framework is flexible and can be easily adjusted to model other urban environments, and additional forms of transportation (such as carpooling, ride-share and more). This general approach can be used to model contiguous disease spread in urban environments, including influenza or various COVID-19 variants.