Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing Behavior

TL;DR

This paper introduces Intend-Wait-Cross, a detailed agent-based model that simulates realistic pedestrian crossing behavior by considering individual traits and environmental factors, with validation against real-world data.

Contribution

The paper presents a novel microscopic pedestrian behavior model that incorporates advanced TTC computation and diverse pedestrian characteristics for realistic simulation.

Findings

Model accurately reproduces pedestrian crossing patterns.

Incorporates context-aware TTC calculation with vehicle dynamics.

Validated against naturalistic driving data.

Abstract

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.