A modified social force model for pedestrian-bicycle mixed flows and its application on evaluating the conflict risk in shared roads

As sustainable modes of transport, walking and cycling are the major choices for short trips. The mixed flow of pedestrians and bicycles is often observed on the roads, but its dynamics and conflict risks have not been well investigated. As commonly observed, pedestrians walk along the road boundaries in the pedestrian-bicycle shared roads. When two or more bicycles share the road, the rear bicycle may choose to either follow or overtake the preceding bicycle under specific criteria. To better reproduce this phenomenon, this study proposed a modified social model. Specifically, self-driving force, force from boundaries, force from other pedestrians and force from bicycles were considered in simulating pedestrian movement. Additionally, the modeling of bicycle movement explicitly takes into account the behavior force for following or overtaking the preceding bicycle. YOLO v5 object detection and DeepSORT multi-object tracking algorithms were applied to extract pedestrian and bicycle trajectories captured by the camera on an unmanned aerial vehicle (UAV). Then the model was calibrated using a genetic algorithm to minimize the discrepancy between the simulated and observed trajectories. Under both unidirectional and bidirectional flow scenarios, the proposed model demonstrates good accuracy in reproducing individual movements and lane-formation phenomena for bidirectional pedestrian-bicycle mixed flows. Furthermore, the calibrated model was applied to evaluate the conflict risk of pedestrians and bicycles in a straight road and an intersection on campus. The safety assessment results indicate that lower density and fewer bicycles in the mixed flow can effectively reduce the risk of conflicts. This study can help understand interactions of pedestrians and cyclists in mixed flow conditions and provide theoretical support for the planning and safety evaluation of pedestrian-bicycle shared roads. © 2024 Elsevier B.V.