Using iterated local alignment to aggregate trajectory data into a traffic flow map

TL;DR

This paper introduces an iterative local alignment method for aggregating vehicle trajectory data into accurate, multi-scale traffic flow maps, effectively handling measurement noise especially at road-level detail.

Contribution

It presents novel local alignment algorithms and an iterative workflow that improve traffic flow map accuracy at multiple scales using noisy GNSS trajectory data.

Findings

High accuracy in flow maps at multiple scales

Effective noise handling in trajectory data

Validated on synthetic and real data

Abstract

Vehicle trajectories are a promising GNSS (Global Navigation Satellite System) data source to compute multi-scale traffic flow maps ranging from the city/regional level to the road level. The main obstacle is that trajectory data are prone to measurement noise. While this is negligible for city level, large-scale flow aggregation, it poses substantial difficulties for road level, small-scale aggregation. To overcome these difficulties, we introduce innovative local alignment algorithms, where we infer road segments to serve as local reference segments, and proceed to align nearby road segments to them. We deploy these algorithms in an iterative workflow to compute locally aligned flow maps. By applying this workflow to synthetic and empirical trajectories, we verify that our locally aligned flow maps provide high levels of accuracy and spatial resolution of flow aggregation at multiple scales for static and interactive maps.