In recent years, micro-mobility transport vehicles have become very popular. As a result, the challenge of developing new strategies and tools for the safety of users and pedestrians in shared travelways has attracted a lot of attention. Previous studies have generally used a social force model as the main tool for the prediction of pedestrian movements. However, existing models cannot be directly applied to wheeled vehicles such as electrical scooters. This study presents a modified social force model to predict the interactions of an electric scooter with a pedestrian crowd by considering the scooter's kinematics constraints and geometry and the velocity-dependent behaviors of the rider. Moreover, experiments are performed to calibrate the parameters of the proposed model and compare it to an existing social force model. The experimental results demonstrate that the scooter social force model is superior to the original model due to its higher prediction accuracy. Using the scooter model with experimentally calibrated parameters, numerical simulations illustrate the behavior of an e-scooter rider in a pedestrian crowd. Acceleration and force metrics are introduced to evaluate the pedestrian comfort and safety. Monte Carlo simulations provide insights for urban planners about how travelway width, pedestrian density, and the e-scooter rider's desired velocity affect pedestrian actual and perceived safety.
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Accepted/In press - 2022|
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications