Version 1
: Received: 28 December 2023 / Approved: 28 December 2023 / Online: 29 December 2023 (05:16:16 CET)
How to cite:
Koliou, K.; Spyropoulou, I. Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics. Preprints2023, 2023122204. https://doi.org/10.20944/preprints202312.2204.v1
Koliou, K.; Spyropoulou, I. Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics. Preprints 2023, 2023122204. https://doi.org/10.20944/preprints202312.2204.v1
Koliou, K.; Spyropoulou, I. Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics. Preprints2023, 2023122204. https://doi.org/10.20944/preprints202312.2204.v1
APA Style
Koliou, K., & Spyropoulou, I. (2023). Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics. Preprints. https://doi.org/10.20944/preprints202312.2204.v1
Chicago/Turabian Style
Koliou, K. and Ioanna Spyropoulou. 2023 "Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics" Preprints. https://doi.org/10.20944/preprints202312.2204.v1
Abstract
Advances in technology have introduced remarkable capabilities, particularly in the realm of gathering and accessing vast amounts of data, which has led to the emergence of the "Big Data" era, presenting exciting opportunities to harness the wealth of the generated information. Traffic management centers comprise a stakeholder that can exploit such data towards improving road network operation. This research, explores the utilization of real traffic data, collected by distinct sources on an interurban motorway: static location based aggregate traffic data and probe vehicle data. The first is collected by traffic detectors, while the second involves patrol vehicles’ GNSS data. The traffic quantity analysed is vehicle speed, while specific emphasis has been given on the ap-plication of heatmaps to represent daily traffic patterns and the utilization of k-means clustering. The performed analysis demonstrates the importance of considering spatiotemporal variables as a unified entity when analysing speed in transportation networks. The limitations of using patrol vehicles as estimators of ambient speed, especially in low and heavy traffic conditions, are deline-ated, while the dual purpose of data gathered from these vehicles towards enhancing driving performance and enabling detector maintenance is emphasized. Valuable traffic insights are offered through comprehensive spatiotemporal speed behavior analysis and data combination. The inte-gration of these techniques enhances decision-making for a streamlined and secure transportation system
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
