Modeling information spread processes in dynamic traffic networks

We propose the probabilistic information spread model to represent the spatiotemporal process of becoming aware while traversing the traffic network. In the contemporary traffic networks drivers are exposed to multiple traffic information sources simultaneously. Traffic managers look for a realistic estimate on when, where and how many drivers become informed about the actual traffic state (e.g. about the event). To this end we propose the probabilistic Information Spread Model (ISM) representing the process of spreading information to the drivers via multiple information sources (radio, VMS, on-line information, mobile applications, etc.). We express the probability of receiving information from a given information source using specifically defined spreading profile (formalized through the probability density function) and market penetration of respective source, with a novel information spreading model for on-line sources (websites, mobile apps, social networks etc.). Moreover, by assuming the information sources are mutually independent, the simplified formula for the joint probability can be used so that the model becomes practically applicable in real-time applications. Model is designed to work within the macroscopic dynamic traffic assignment (DTA) as a part of the network flow propagation model. Thanks to that, the informed drivers can be traced as they propagate through the network towards their destinations. We illustrate the model with the simulations on Dusseldorf network showing how information is spread in several ATIS scenarios (VMS, radio news, online sources, and simultaneous sources.