How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic

TL;DR

This paper introduces PriorMotion, a novel data integration framework that improves the analysis of urban traffic dynamics by capturing complex spatial-temporal patterns, thereby aiding infrastructure management and policy development.

Contribution

PriorMotion offers a systematic approach combining multi-scale empirical data with customized tools to better analyze and predict urban traffic movements, overcoming limitations of traditional grid-based methods.

Findings

Enhanced accuracy in traffic pattern analysis

Improved adaptability to diverse data sources

Reduced long-term projection errors

Abstract

Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analyzing traffic patterns have gained traction due to their applicability to city-scale policy development. However, conventional methodologies often rely on fragmented grid-based techniques, which may overlook critical interdependencies among spatial elements and temporal continuity. These limitations can compromise analytical effectiveness in complex urban environments. To address these challenges, we propose PriorMotion, a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis. Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic. Comprehensive evaluations demonstrate that PriorMotion significantly enhances analytical outcomes, including increased accuracy in traffic pattern analysis, improved adaptability to heterogeneous data environments, and reduced long-term projection errors. Validation confirms its effectiveness for urban infrastructure management applications requiring precise characterization of complex spatial-temporal interactions.