A simulation of urban incidents involving pedestrians and vehicles based on Weighted A*

TL;DR

This paper introduces a multiagent simulation framework for urban incidents involving pedestrians and vehicles, utilizing weighted A* algorithms to model decision-making and interactions in a detailed 2D environment.

Contribution

It presents a novel multiagent simulation model incorporating weighted A* pathfinding to analyze urban traffic incidents and safety under various conditions.

Findings

Higher obstacle density increases collision risk.

Traffic control mechanisms improve safety and efficiency.

Behavioral deviations impact collision likelihood and travel times.

Abstract

This document presents a comprehensive simulation framework designed to model urban incidents involving pedestrians and vehicles. Using a multiagent systems approach, two types of agents (pedestrians and vehicles) are introduced within a 2D grid based urban environment. The environment encodes streets, sidewalks, buildings, zebra crossings, and obstacles such as potholes and infrastructure elements. Each agent employs a weighted A* algorithm for pathfinding, allowing for variation in decision making behavior such as reckless movement or strict rule-following. The model aims to simulate interactions, assess risk of collisions, and evaluate efficiency under varying environmental and behavioral conditions. Experimental results explore how factors like obstacle density, presence of traffic control mechanisms, and behavioral deviations affect safety and travel efficiency.