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INTELLIGENT TRANSPORTATION SYSTEM DATA ARCHIVING: STATISTICAL TECHNIQUES FOR DETERMINING OPTIMAL AGGREGATION WIDTHS FOR INDUCTIVE LOOP DETECTOR SPEED DATA.
| 题名： | INTELLIGENT TRANSPORTATION SYSTEM DATA ARCHIVING: STATISTICAL TECHNIQUES FOR DETERMINING OPTIMAL AGGREGATION WIDTHS FOR INDUCTIVE LOOP DETECTOR SPEED DATA. |
| 作者： | Gajewski-BJ; Turner-SM; Eisele-WL; Spiegelman-CH |
| 关键词： | Data-aggregation; Data-collection; Data-storage; Intelligent-transportation-systems; Loop-detectors; San-Antonio-Texas; Statistical-techniques; Traffic-data; Traffic-speed |
| 摘要： | Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results--both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., "How much do we/the market value the data?"), ease of implementation, system requirements, and other constraints. |
| 总页数： | Transportation Research Record. 2000. (1719) pp85-93 (4 Fig., 2 Tab., 9 Ref.) |
| 报告类型： | 科技报告 |
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