  In this paper, we present a microscopic agent-based pedestrian behavior model
Intend-Wait-Cross. The model is comprised of rules representing behaviors of
pedestrians as a series of decisions that depend on their individual
characteristics (e.g. demographics, walking speed, law obedience) and
environmental conditions (e.g. traffic flow, road structure). The model's main
focus is on generating realistic crossing decision-model, which incorporates an
improved formulation of time-to-collision (TTC) computation accounting for
context, vehicle dynamics, and perceptual noise.
  Our model generates a diverse population of agents acting in a highly
configurable environment. All model components, including individual
characteristics of pedestrians, types of decisions they make, and environmental
factors, are motivated by studies on pedestrian traffic behavior. Model
parameters are calibrated using a combination of naturalistic driving data and
estimates from the literature to maximize the realism of the simulated
behaviors. A number of experiments validate various aspects of the model, such
as pedestrian crossing patterns, and individual characteristics of pedestrians.
