Framework for Evaluating the Reliability of Wide-Area Probe Data

This paper presents a framework for evaluating the reliability of probe-sourced traffic speed data for detection of congestion and assessment of roadway performance. The methodology outlined uses pattern recognition to quantify accurately the similarities and dissimilarities of probe-sourced and benchmarked local sensor data. First, a pattern recognition algorithm called empirical mode decomposition was used to define short-, medium-, and long-term trends for the probe-sourced and infrastructure-mounted local sensor data sets. The reliability of the probe data was then estimated on the basis of the similarity or synchrony between corresponding trends. The synchrony between long-term trends was used as a measure of accuracy for general performance assessment, whereas short- and medium-term trends were used for testing the accuracy of congestion detection with probe-sourced data. By using 1 month of high-resolution speed data, the authors were able to use probe data to detect, on average, 74% and 63% of the short-term events (events lasting for at most 30 min) and 95% and 68% of the medium-term events (events lasting between 1 and 3 h) on freeways and nonfreeways, respectively. Significant latencies do, however, exist between the data sets. On nonfreeways, the benchmarked data detected events, on average, 12 min earlier than the probe data. On freeways, the latency between the data sets was reduced to 8 min. The resulting framework can serve as a guide for state departments of transportation when they outsource collection of traffic data to probe-based services or supplement their data with data from such services.