Physical and Transportation Systems

Predicting Commuter Flow

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and of human mobility. CNDS researchers areĀ focused on developing first principle based methods for traffic prediction using a cost based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. This work can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

Higher Order Network for Transportation Systems

Network-based representation has quickly emerged as the norm in representing rich interactions among the components of a complex system for analysis and modeling: movement of hundreds of thousands of ships form a global shipping network, powering the transportation and economy while inadvertently translocating invasive species; interactions of billions of people on social networks, facilitating the diffusion of information. Research by the CNDS in higher-order network preserves higher-order dependencies in the network structure, therefore movements simulated on the network more accurately reflects the true movement patterns in the raw data. The promise of higher order representation of networks (HON) research is to be able to capture non-Markovian, higher- and variable- order characteristics, transitions, and dependencies in the data.