An adaptive smoothing method for traffic state identification from incomplete information

TL;DR

This paper introduces an adaptive smoothing method that uses nonlinear spatio-temporal filtering to reconstruct and visualize traffic states from incomplete detector data, effectively handling up to 65% data loss.

Contribution

The paper presents a novel adaptive smoothing technique that exploits traffic flow directions to improve traffic state estimation from sparse data.

Findings

Successfully visualizes traffic patterns with up to 65% data missing

Robust results for detector spacing up to 3 km

Effective in real-world German freeway congestion scenarios

Abstract

We present a new method to obtain spatio-temporal information from aggregated data of stationary traffic detectors, the ``adaptive smoothing method''. In essential, a nonlinear spatio-temporal lowpass filter is applied to the input detector data. This filter exploits the fact that, in congested traffic, perturbations travel upstream at a constant speed, while in free traffic, information propagates downstream. As a result, one obtains velocity, flow, or other traffic variables as smooth functions of space and time. Applications include traffic-state visualization, reconstruction of traffic situations from incomplete information, fast identification of traffic breakdowns (e.g., in incident detection), and experimental verification of traffic models. We apply the adaptive smoothing method to observed congestion patterns on several German freeways. It manages to make sense out of data where conventional visualization techniques fail. By ignoring up to 65% of the detectors and applying the method to the reduced data set, we show that the results are robust. The method works well if the distances between neighbouring detector cross sections do not exceed 3 km.