In recent years, the use of personal mobility devices, particularly electric scooters, has become increasingly popular, especially for short-distance travel commonly referred to as the “last mile.” This growing trend has led to complex interactions between scooter riders and pedestrians, as pedestrians often exhibit unpredictable behavior, such as remaining stationary or walking at varying speeds, which increases the risk of collisions with fast-moving scooters. These interactions can lead to disturbances in pedestrian flow, irritation, or even accidents, particularly in environments lacking effective regulation or infrastructure. This study presents an advanced prediction model for pedestrian movement and the dynamics between pedestrians and electric scooters. The model is an enhanced version of the Social Force Model, incorporating the distinct movement characteristics of electric scooters, such as high speed and rapid acceleration, especially on curved roads. Simulation results showed that in low-density pedestrian environments, personal mobility devices can overtake pedestrians without significant disruptions, whereas in high-density areas, scooters and Segways face challenges overtaking pedestrians due to congestion. This study also highlights that pedestrian density plays a crucial role in the movement dynamics of electric scooters, with congestion reducing their ability to navigate efficiently. The results provide valuable insights into pedestrian safety risks, such as collisions and congestion, and offer a deeper understanding of mobility dynamics in urban environments. The findings emphasize the need for improved infrastructure and regulation to ensure safe and efficient coexistence of pedestrians and personal mobility devices.
Integrating Pedestrian and Scooter Traffic: A Model for Safe Urban Mobility
Communic.Comp.Inf.Science
International Conference on Optimization and Data Science in Industrial Engineering ; 2024 ; Istanbul, Türkiye November 07, 2024 - November 08, 2024
2025-08-06
20 pages
Article/Chapter (Book)
Electronic Resource
English
Shared Urban Spaces , Social Force Model , Personal Mobility Devices , Pedestrian Traffic Modeling Artificial Intelligence , Computer Science , Machine Learning , Information Systems and Communication Service , Cyber-physical systems, IoT , Data Structures and Information Theory , Professional Computing