Visualizing spatiotemporal flows of on-demand deliveries: A 3D interactive animation of path-reconstructed rider trajectories in Beijing

Abstract

Instant delivery has emerged as a critical component of urban mobility, with its spatiotemporal dynamics significantly shaping street congestion, emissions, and gig labor conditions. Here, I developed a 3D interactive animation to vividly visualize the spatiotemporal trajectories of on-demand deliveries based on 2508 real-world orders delivered by 113 unique riders in Beijing. This visualization transforms fragmented origin–destination (OD) order data into a dynamic exploration of street-level delivery mobility, revealing riders’ multi-stop routing patterns, street flow distributions, and collective urban delivery rhythms. This case demonstrates the potential of dynamic visualization in urban trajectory analytics.