Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

Abstract

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.

Double-Sided Determination of Aggregation Level for Intelligent Transportation System Data

“…The initial practice for aggregation of archived ITS data includes aggregating raw ITS data to a level that allows typical users to meet existing data needs, and saving raw ITS data as they are collected to provide maximum flexibility for data exploration and mining (6). Gajewski et al introduced two statistical techniques that can be used to determine optimal aggregation levels based on the variability of traffic parameters (6). These conventional techniques are intuitive and easy to implement.…”

Length and speed detection using microwave motion sensor

“…We observe amount of time between two consecutive changes of inductances of loops and correlate with the distance between two loops to calculate length and speed of vehicle passing through the system. However, the difference in size, model between vehicles makes this method more difficult, so the major use of inductive loop detector is to detect rather than to measure [5] [6] [7].…”

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

“…Recently, a number of methods have been used to identify the optimal aggregation level of loop detector data (rather than probe-based data). Gajewski et al (2000) use a cross-validated mean square error approach to choose optimal aggregation widths in estimating speed data from loop detectors for data archiving purposes. Qiao et al (2003) and (2004) and Oh et al (2005) applied various statistical methods for this purpose.…”