Talking Trails: LLM-Enhanced Spatiotemporal Trajectory Modeling for E-Bike Delivery Route Planning

Abstract

Electric bicycles (e-bikes) have become the dominant mode of transportation in China’s urban instant delivery industry. However, many riders lack the experience to navigate complex traffic networks and diverse road conditions, leading to reduced delivery efficiency. To address this issue, we present Talking Trails, an e-bike delivery route planning system built upon an LLM-enhanced spatiotemporal trajectory model. Trained on millions of real-world delivery trajectories, fused with spatiotemporal and semantic data information, the model achieves a top-5 rider displacement prediction accuracy of 95% and a route optimization rate of 82.1%. In practice, we augment the core planner with an LLM-driven semantic layer that translates high-level user intent into executable tasks, then pair it with a battery-swap module that continuously validates route feasibility so the vehicle never runs out of charge mid-mission. Currently serving tens of thousands of riders, the system is projected to reduce average delivery mileage by 17% and lower annual carbon emissions by 3978 tons. Overall, Talking Trails significantly improves delivery efficiency, offering a scalable and sustainable solution for instant delivery operations.