The average annual daily bicyclist volume (AADB) is a measure being used in non-motorized transportation studies such as exposure modeling. This metric can be estimated by averaging the daily bicyclist's volume measured throughout the year with a long-term automated counter. As continuous data for a whole year at many sites may not be available, a common practice is to collect short-term count data for a sample of locations and then apply an extrapolation method to convert short-term count data to yearly count data. To perform extrapolation, each short-term counter must be matched to one or more long-term counters with similar demand patterns. The matching procedure can significantly impact the accuracy of AADB estimation. This study proposes a matching approach based on a cluster analysis approach, Partitioning Around Medoids (PAM), and a supervised learning approach, K-Nearest Neighbor (KNN). It was found that the proposed approach using a combination of certain variables such as land use and a traffic distribution index resulted in low AADB estimation error rates, which enhanced existing approaches.
Developing Models for Matching of Short-term and Long-term Data Collection Sites to Improve the Estimation of Average Annual Daily Bicyclists
2018-11-01
402597 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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