Reliability of Probe Speed Data for Detecting Congestion Trends

Abstract

This paper presents a framework for evaluating the reliability of probe-sourced traffic speed data for congestion detection and general infrastructure performance assessment. The methodology outlined employs pattern recognition and time-series analysis to accurately quantify the similarity and dissimilarities between probe-sourced and benchmarked local sensor data. First, an adaptive and multiscale pattern recognition algorithm called Empirical Mode Decomposition (EMD) is used to define short, medium and long-term trends for the probe-sourced and infrastructure mounted local sensor datasets. The reliability of the probe data is then estimated based on the similarity or synchrony between corresponding trends. The synchrony between long-term trends are used as a measure of accuracy for general performance assessment, whereas short and medium term trends are used for testing the accuracy of congestion detection with probe-sourced data. Using one-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 minutes), 95% and 68% of the medium-term events (events lasting between 1 and 3 hours) on freeways and non - freeways respectively. Significant latencies do however exist between both datasets. On non - freeways, the benchmarked data detected events, on average, 12 minutes earlier than the probe data. On freeways, the latency between the datasets was reduced to 8 minutes. The resulting framework can serve as a guide for state DOTs when outsourcing or supplementing traffic data collection to probe-based services.