It becomes common practice to equip our cities and streets with a wide range of sensors (e.g., cameras, inductive loops and push buttons for pedestrians). Those sensors are paving the way for smart monitoring of traffic, which is nowadays already used to, for example, dynamically steer traffic flows by controlling traffic lights or track stolen vehicles. However, such data remains very complex to thoroughly inspect and interpret. In this paper we exploit an advanced spatio-temporal disaggregation technique to extract insights from ANPR data. More specifically, we visualise and analyse spatial and temporal insights from vehicle detections along street segments in a dense network of ANPR cameras in the police district of Voorkempen, Belgium. The proposed analysis tools can be integrated in an intelligent transportation system (ITS) to support traffic monitoring by the appropriate authorities.
Dhont, M, Dhont, M, Tsiporkova, E, Gonzalez-Deleito, N & Cornelis, B 2022, Making Sense of ANPR Data via Intelligent Spatio-temporal Disaggregation of Traffic Flows. in 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022. 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE Xplore, Macau, China, pp. 1433-1439, IEEE 25th international conference on intelligent transportation Systems, Macao, China, 8/10/22. https://doi.org/10.1109/itsc55140.2022.9921789
Dhont, M., Dhont, M., Tsiporkova, E., Gonzalez-Deleito, N., & Cornelis, B. (2022). Making Sense of ANPR Data via Intelligent Spatio-temporal Disaggregation of Traffic Flows. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022 (pp. 1433-1439). (2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)). IEEE Xplore. https://doi.org/10.1109/itsc55140.2022.9921789
@inproceedings{c55368369dfb4d39989a34aa0978c1d1,
title = "Making Sense of ANPR Data via Intelligent Spatio-temporal Disaggregation of Traffic Flows",
abstract = "It becomes common practice to equip our cities and streets with a wide range of sensors (e.g., cameras, inductive loops and push buttons for pedestrians). Those sensors are paving the way for smart monitoring of traffic, which is nowadays already used to, for example, dynamically steer traffic flows by controlling traffic lights or track stolen vehicles. However, such data remains very complex to thoroughly inspect and interpret. In this paper we exploit an advanced spatio-temporal disaggregation technique to extract insights from ANPR data. More specifically, we visualise and analyse spatial and temporal insights from vehicle detections along street segments in a dense network of ANPR cameras in the police district of Voorkempen, Belgium. The proposed analysis tools can be integrated in an intelligent transportation system (ITS) to support traffic monitoring by the appropriate authorities.",
keywords = "Law enforcement, Visual analytics, Urban areas, Cameras, Market research, Data mining, Intelligent transportation systems, ANPR data",
author = "Michiel Dhont and Michiel Dhont and Elena Tsiporkova and Nicolas Gonzalez-Deleito and Bruno Cornelis",
note = "Funding Information: This research was subsidised through the project MISTic by the Brussels-Capital Region – Innoviris and received funding from the Flemish Government (AI Research Program). Publisher Copyright: {\textcopyright} 2022 IEEE. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.; IEEE 25th international conference on intelligent transportation Systems, ITSC ; Conference date: 08-10-2022 Through 12-10-2022",
year = "2022",
month = oct,
day = "8",
doi = "10.1109/itsc55140.2022.9921789",
language = "English",
isbn = "978-1-6654-6881-7",
series = "2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)",
publisher = "IEEE Xplore",
pages = "1433--1439",
booktitle = "2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022",
}