Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training

Abstract

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multimodal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic co-

Strategic Roadmap for Adopting Data-Driven Proactive Measures in Solar Logistics

“…Multi-Modal Transport Solutions Utilizing a combination of transport methods (road, rail, air) for equipment delivery, especially to remote or hard-to-reach locations [59].…”