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Abstract
The paper addresses the task of improving the quality of traffic data by combining semantic analysis and statistical modeling. The study focuses on dynamic traffic variables, specifically speed and volume of traffic flow. The results are presented based on semantic quality control outputs for dynamic traffic variables. The semantic analysis aimed to find and label segments of time series waveforms that fell outside the expected value range at specific locations and times. This approach allowed for a more detailed analysis of specific cases, enhancing our understanding of traffic dynamics. We employed a Generalized Additive Model (GAM) to detect non-standard data segments successfully. Real traffic data from the motorway in the Czech Republic verified the effectiveness of the chosen statistical approach.
Highlights
Quality public information services rely on accurate data regarding the condition and performance of the transport system
The findings indicate that master data management (MDM) can effectively address data quality issues arising from the fragmentation of original data sources
This paper addresses that gap by demonstrating the applicability of data governance strategies for transportation data
Summary
Quality public information services rely on accurate data regarding the condition and performance of the transport system. Integrating data from various sources into useful datasets is a significant challenge. This must be addressed to ensure traffic management and passenger information services are credible and efficient. Systems that automatically disseminate passenger information based on traffic monitoring inputs require high data reliability. The quality of traffic data is critical for informed decision-making. This includes optimizing traffic flow, planning infrastructure improvements, or enhancing road safety measures. Effective automated methods are crucial to prevent the publication of erroneous or misleading information
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