Abstract
Probe-based speed data provide great value to agencies; especially, in areas which are not feasibly covered by traffic sensors. However, as with sensors, probe data are not without nuance and issues like latency prevent alignment between calculated metrics by data source. In recent years, there has been a strong impetus on using data-driven decision making. Data-driven insights have become critical for smart mobility. To support data-driven decision making, Federal Highway Administration has procured probe data feeds and provides free access to state and local agencies as National Performance Measures Research dataset (NPRMDS). In addition to the NPRMDS, several state agencies subscribe to a paid probe data provider for obtaining real-time streams of high-resolution probe data. These datasets are used to generate nationwide urban mobility reports as well as reports focusing on certain jurisdiction. These mobility reports are integrated in several Transportation System Management and Operations (TSMO) plans which are often used to drive several resource allocation projects. This paper examines accuracy of methodology used to derive two frequently used performance measures in the mobility reports; namely, number of congested hours and number of congestion events. An improve methodology is then proposed to find accurate estimates for number of congested hours and number of congested incidents.
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Our research results are based on the work supported by the Iowa DOT Office of Traffic Operations Support Grant. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Iowa DOT.
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Ahsani, V., Sharma, A., Hegde, C. et al. Improving Probe-Based Congestion Performance Metrics Accuracy by Using Change Point Detection. J. Big Data Anal. Transp. 2, 61–74 (2020). https://doi.org/10.1007/s42421-020-00017-w
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DOI: https://doi.org/10.1007/s42421-020-00017-w