Reconstructing the spatio-temporal traffic dynamics from stationary detector data

We present a new method to obtain spatio-temporal information from aggregated data of stationary traffic detectors, the "adaptive smoothing method". In essential, a nonlin-ear 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 near-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 identifica-tion of traffic breakdowns (e.g., in incident detection), and experimental verification of traffic models, and even a short-term traffic forecast. 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.