🤖 AI Summary
This study addresses the dual challenges of collision risk and traffic efficiency arising from pedestrian–vehicle interactions in urban environments by developing a two-dimensional grid-based multi-agent simulation framework that explicitly models streets, sidewalks, crosswalks, and obstacles. The authors introduce a weight-adjustable A* algorithm to parameterize diverse behavioral patterns—ranging from cautious to reckless—for both pedestrians and vehicles, enabling dynamic path planning and realistic modeling of complex traffic interactions. Experimental results demonstrate that obstacle density, traffic control infrastructure, and behavioral deviations significantly influence both collision likelihood and throughput efficiency. The proposed approach exhibits strong adaptability and effectiveness across multiple scenarios, offering a scalable and extensible simulation platform for safety evaluation in intelligent transportation systems.
📝 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.