01548818

Component

https://doi.org/10.3141/2443-09

http://scholar.google.com...ttak&publication_year=2014

Transportation engineers and researchers heavily use traffic data, which are generally aggregated by predetermined time intervals (e.g., 5 to 15 min). The aggregation process often discards essential information of traffic state transition (e.g., breakdowns). However, the transition of traffic conditions within an aggregation interval is not well understood. This study explored traffic state transition from uncongested to congested regimes that occurred within a predetermined time interval. From two urban freeway locations in Norfolk, Virginia, traffic data archived at 15-min intervals were obtained. A heuristic method based on a Gaussian mixture model was developed to detect the aggregate traffic data that exhibit the transition of traffic states as well as to partition the data statistically into uncongested and congested traffic states. Results show a substantial difference in travel speed (approximately 20 mph) between the two states. In addition, these results illustrate that aggregating these different traffic conditions can cause substantial traffic data aggregation bias by lowering travel speed and flow rates, especially in high traffic flow situations. Finally, new insights into valid traffic data aggregation and speed-flow-concentration relationship development are discussed.

01548337

English

0000-0002-0790-7794

pp 78-87

2014

Transportation Research Record: Journal of the Transportation Research Board

9780309295314

Print

Figures; References; Tables

Bias (Statistics); Data processing operations; Time intervals; Traffic data; Traffic flow rate; Traffic speed

Data aggregation

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 24 2014 9:12AM

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