Spatial-Temporal Nonlocal Traffic Dynamics: Analytical Properties, Adaptive Kernel Formulation, and Empirical Validation

TL;DR

This paper introduces a novel spatial-temporal nonlocal traffic flow model with an adaptive kernel, providing a more realistic depiction of driver behavior and validated through empirical data, outperforming traditional models.

Contribution

The paper develops a new nonlocal traffic model with an adaptive kernel and establishes its analytical properties, validated by empirical data to improve traffic density reconstruction.

Findings

Model captures driver anticipation effects more accurately.

Empirical validation shows improved traffic density reconstruction.

Significantly outperforms traditional local models in key regimes.

Abstract

This paper presents a new spatial-temporal nonlocal traffic flow model formulated to overcome the boundedness limitations inherent in classical local formulations. The model introduces an adaptive kernel that captures both spatial and temporal nonlocal interactions, allowing the velocity at a given point to depend on aggregated downstream traffic conditions over a finite time horizon. This structure provides a more realistic representation of driver anticipation and reaction behavior. In addition to developing the model, we establish several key analytical properties that clarify the theoretical foundations of the proposed nonlocal framework. To assess its practical relevance, we conduct a detailed empirical validation using high-resolution NGSIM trajectory data. The results demonstrate that the spatial-temporal nonlocal model significantly improves the reconstruction of traffic density fields compared with traditional local macroscopic models, particularly in regimes where anticipation effects dominate. These findings highlight the potential of spatial-temporal nonlocal traffic dynamics as a robust theoretical and data-driven framework for capturing complex traffic behavior.