arXiv:2510.14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. From a behavioral perspective, a trajectory reflects a sequence of route choices within an urban environment. However, most existing TRL methods ignore this underlying decision-making process and instead treat trajectories as static, passive spatiotemporal sequences, thereby limiting the semantic richness of the learned re
The increasing availability of large-scale trajectory datasets and advancements in AI/ML techniques are enabling more sophisticated analyses of mobility patterns.
Improving trajectory representation learning can significantly enhance the accuracy and utility of applications like urban planning, logistics optimization, and personalized navigation, impacting economic efficiency and resource allocation.
This research shifts trajectory analysis from static spatiotemporal sequences to intelligent decision-making processes, leading to richer embeddings that capture underlying behavioral semantics.
- · Logistics companies
- · Urban planners
- · Ride-sharing platforms
- · Autonomous vehicle developers
- · Traditional static mapping services
- · Inefficient transportation systems
More accurate predictions of traffic flow and individual mobility patterns.
Optimized urban infrastructure design based on a deeper understanding of human movement.
New forms of personalized services and smart city applications leveraging fine-grained behavioral insights.
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Read at arXiv cs.LG