Relation-Aware LNN-Transformer for Intersection-Centric Next-Step Prediction

TL;DR

This paper introduces a novel relation-aware LNN-Transformer model that improves next-step human mobility prediction by incorporating road network topology and environmental context, outperforming existing methods on city-scale data.

Contribution

The paper presents a road-node-centric framework with a new sequence model combining a CfC-LNN and bearing-biased self-attention for enhanced mobility prediction.

Findings

Outperforms six state-of-the-art baselines by up to 17% in accuracy.

Maintains high resilience under GPS noise and POI perturbations.

Effectively captures both temporal dynamics and spatial dependencies.

Abstract

Next-step location prediction plays a pivotal role in modeling human mobility, underpinning applications from personalized navigation to strategic urban planning. However, approaches that assume a closed world - restricting choices to a predefined set of points of interest (POIs) - often fail to capture exploratory or target-agnostic behavior and the topological constraints of urban road networks. Hence, we introduce a road-node-centric framework that represents road-user trajectories on the city's road-intersection graph, thereby relaxing the closed-world constraint and supporting next-step forecasting beyond fixed POI sets. To encode environmental context, we introduce a sector-wise directional POI aggregation that produces compact features capturing distance, bearing, density and presence cues. By combining these cues with structural graph embeddings, we obtain semantically grounded node representations. For sequence modeling, we integrate a Relation-Aware LNN-Transformer - a hybrid of a Continuous-time Forgetting Cell CfC-LNN and a bearing-biased self-attention module - to capture both fine-grained temporal dynamics and long-range spatial dependencies. Evaluated on city-scale road-user trajectories, our model outperforms six state-of-the-art baselines by up to 17 percentage points in accuracy at one hop and 10 percentage points in MRR, and maintains high resilience under noise, losing only 2.4 percentage points in accuracy at one under 50 meter GPS perturbation and 8.9 percentage points in accuracy at one hop under 25 percent POI noise.