To manage and operate ITS technologies to exceed customer expectations, data is needed on the structure, status, use and behavior of the entire transportation system. With the advent of Archived Data User Service (ADUS), the National ITS Architecture officially embraced the concept of saving (retaining and archiving) real-time, operational data for other non-real-time applications, such as transportation planning, safety analyses, and research. There are three major aspects to fulfilling data needs: understanding what data is needed by the full range of stakeholders; creating/ maintaining the mechanisms to gather/ store the data; and providing convenient, timely and affordable access to the data. The report provides examples of who can profit from data, what types of data they would like and what benefits can accrue if these users obtain the quality of data they seek. This report, performed for the Wisconsin Department of Transportation, overviews the current data collection and management environment for improved data archiving. Among the lessons learned are the need to improve data quality, the need to provide ITS data at different aggregation levels, and the need to provide easy and low-cost access to ITS data.
This document provides some best practices for Intelligent Transportation Systems (ITS) data management and archiving, and articulates the relevance of good data collection procedures to transportation operations and management. Two specific topics discussed include data quality and data referencing.
Assess the quality of the ITS data. Many users are concerned over the quality of ITS data and geographic location referencing. The most frequently cited reasons for insufficient data quality are inadequate geographic coverage, inaccurate information, insufficient update frequency, lack of timeliness, and inadequate spatial resolution. The issue concerning geographic coverage arises mainly from incomplete data collection in metropolitan areas with multiple jurisdictions. Some of the concern about the data quality may be attributed to the fact that the large ITS data sets are new to many users, thus there is some unfamiliarity with the inherent quality of data. The concern with data quality can also be related to performing minimal error detection as the data is being collected. Quality control procedures are especially critical with ITS data for several reasons:
- The large potential volume of ITS data makes it difficult to detect errors using traditional manual techniques
- The continuous monitoring nature of ITS data implies that equipment errors and malfunctions are more likely during operation than periodic data collection efforts
- Archived data users may have different (and potentially more stringent) data quality requirements than real-time users
Consider there may be difficulties when referencing actual data. It was often noted that referencing actual data obtained from the TMC with a location on a roadway segment was difficult. Secondary users of data often have to develop equivalency matrices to compare several location referencing schemes.
This lesson suggests that consideration be taken regarding the quality of real-time ITS data being collected and the difficulties associated with referencing the data. It also suggests that ITS data quality be improved to achieve better analysis standards, and improve the quality of the analyses and research results drawn from these analyses.
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