The impact of travelers’ rationality degree heterogeneity in the Advanced Traveler Information System on the network traffic flow evolution

The interplay between traffic information, which is normally distributed by the Advanced Traveler Information System (ATIS) and travelers’ decision behaviors, is prone to lead to high complexity in the evolution process of network traffic flow. Considering the obvious heterogeneity that is reflected in the numerous ways that travelers adopt ATIS information and choose routes, the lognormal distribution is adopted to describe the heterogeneity of travelers’ rationality degree. Introducing habitual factors of traveler route choice, modeling ideas of Multi-Agent and Mixed Logit are utilized to construct the day-to-day evolution model of network traffic flow, which is based on the value difference of travelers’ cognitive travel time. Furthermore, an integrated simulation algorithm based on the Monte Carlo method is specially designed to solve the previous evolution model. The simulation indicates that a lower individual difference and a higher rationality degree would lead to a more obvious aggregation phenomenon of network traffic flow and inefficiency of operation in road networks.