  Human route choice is undeniably one of the key contributing factors towards
traffic dynamics. However, most existing macroscopic traffic models are
typically concerned with driving behavior and do not incorporate human route
choice behavior models in their formulation. In this paper, we propose a
socio-technical macroscopic traffic model that characterizes the traffic states
using human route choice attributes. Essentially, such model provides a
framework for capturing the Cyber-Physical-Social coupling in smart
transportation systems. To derive this model, we first use Cumulative Prospect
Theory (CPT) to model the human passengers' route choice under bounded
rationality. These choices are assumed to be influenced by traffic alerts and
other incomplete traffic information. Next, we assume that the vehicles are
operating under a non-cooperative cruise control scenario. Accordingly, human
route choice segregates the traffic into multiple classes where each class
corresponds to a specific route between an origin-destination pair. Thereafter,
we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain
a macroscopic model which embeds the human route choice attribute. Finally, we
analyze the mathematical characteristics of the proposed model and present
simulation studies to illustrate the model behavior.
