2007Transportation Research Board 86th Annual MeetingTransportation Research BoardRequires access

TRAFFIC DATA COLLECTION: QUALITY ASPECTS OF VIDEO DETECTION

J Versavel

Open publisher page 5 citations

Abstract

Because of continuous increasing traffic volume and complexity, traffic managers worldwide are faced with an increasing demand for state-of-the-art intelligent traffic information, mainly for traffic management and for safety issues. Quality is fitness for use. Different detection technologies show different characteristics and prove to be successful in different application areas. Traditionally, inductive loops provide ample information to direct traffic flows and assemble statistics. However the information produced by inductive loops are too limited for several more complex applications such as incident detection, hard shoulder monitoring, pedestrian detection, etc. Current complex traffic situations need more extensive information. This paper is based on the report of the “Traffic Data Quality Workshop” for the federal Highway Administration (FHWA). This report defines traffic data quality as follows:”Data quality is the fitness of data for all purposes that require it. Measuring data quality requires an understanding of all intended purposes for that data. The following data quality measurement parameters are recommended: accuracy, completeness, validity, timeliness, coverage, accessibility. In this paper we analyse each of these parameters linked to video detection for traffic measurement.

About this research paper

What this paper is about

Because of continuous increasing traffic volume and complexity, traffic managers worldwide are faced with an increasing demand for state-of-the-art intelligent traffic information, mainly for traffic management and for safety issues. Quality is fitness for use. Different detection technologies show different characteristics and prove to be successful in different application areas. Traditionally, inductive loops provide ample information to direct traffic flows and assemble statistics. However the information produced by inductive loops are too limited for several more complex applications such as incident detection, hard shoulder monitoring, pedestrian detection, etc. Current complex traffic situations need more extensive information. This paper is based on the report of the “Traffic Data Quality Workshop” for the federal Highway Administration (FHWA). This report defines traffic data quality as follows:”Data quality is the fitness of data for all purposes that require it. Measuring data quality requires an understanding of all intended purposes for that data. The following data quality measurement parameters are recommended: accuracy, completeness, validity, timeliness, coverage, accessibility. In this paper we analyse each of these parameters linked to video detection for traffic measurement.

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Available abstract

Because of continuous increasing traffic volume and complexity, traffic managers worldwide are faced with an increasing demand for state-of-the-art intelligent traffic information, mainly for traffic management and for safety issues. Quality is fitness for use. Different detection technologies show different characteristics and prove to be successful in different application areas. Traditionally, inductive loops provide ample information to direct traffic flows and assemble statistics. However the information produced by inductive loops are too limited for several more complex applications such as incident detection, hard shoulder monitoring, pedestrian detection, etc. Current complex traffic situations need more extensive information. This paper is based on the report of the “Traffic Data Quality Workshop” for the federal Highway Administration (FHWA). This report defines traffic data quality as follows:”Data quality is the fitness of data for all purposes that require it. Measuring data quality requires an understanding of all intended purposes for that data. The following data quality measurement parameters are recommended: accuracy, completeness, validity, timeliness, coverage, accessibility. In this paper we analyse each of these parameters linked to video detection for traffic measurement.

Key concepts: Computer science, Data quality, Quality (philosophy), Data collection, Data mining, Completeness (order theory), Data science, Engineering

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