Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance
Smart mobility initiatives encompass innovative methods to support traffic management experts in decisions for how to improve urban infrastructures and reduce carbon footprint. Accurate and continuous information about traffic is necessary to implement effectively such decisions. This is not always...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10360829/ |
| _version_ | 1797376326907199488 |
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| author | Davide Andrea Guastella Evangelos Pournaras |
| author_facet | Davide Andrea Guastella Evangelos Pournaras |
| author_sort | Davide Andrea Guastella |
| collection | DOAJ |
| description | Smart mobility initiatives encompass innovative methods to support traffic management experts in decisions for how to improve urban infrastructures and reduce carbon footprint. Accurate and continuous information about traffic is necessary to implement effectively such decisions. This is not always possible because of the cost of the information: it is not possible to install sensor devices at large scale because of financial costs and privacy; employing a plethora of sensors requires significant computational capabilities to process the generated data. A centralized data analysis can hinder real-time applications, and limit their practical deployment in traffic management systems. This paper introduces a novel privacy-aware method for estimating traffic density using edge computing and without over-deploying privacy-intrusive surveillance technologies such as cameras. The objective is to reduce the cost of collecting data while providing accurate information to support traffic operators in decision making. We evaluate the proposed solution using a realistic traffic data of Bologna in Italy. Results shows that it yields a 45% lower average estimation error compared to standard prediction methods. Virtual traffic monitoring devices are associated with software agents that collect data from simulated traffic and estimate traffic density measurements when this information is not available. In our experiments, when we replace 50% of camera devices with cooperative low-cost edge devices, we obtain an average percentage error of just 22%. This result indicates that the cooperation between virtual traffic monitoring devices offers a means to avoid massive deployment of camera surveillance devices using low-cost information provided by connected vehicles. We also compared the results to those obtained by standard regression techniques. |
| first_indexed | 2024-03-08T19:36:57Z |
| format | Article |
| id | doaj.art-05c6f1f270ef4fd9a379a3fb3cb91e4f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:36:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-05c6f1f270ef4fd9a379a3fb3cb91e4f2023-12-26T00:07:52ZengIEEEIEEE Access2169-35362023-01-011114212514214510.1109/ACCESS.2023.334362010360829Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera SurveillanceDavide Andrea Guastella0https://orcid.org/0000-0002-6865-1833Evangelos Pournaras1https://orcid.org/0000-0003-3900-2057Machine Learning Group, Université Libre de Bruxelles, Brussels, BelgiumSchool of Computing, University of Leeds, Leeds, U.KSmart mobility initiatives encompass innovative methods to support traffic management experts in decisions for how to improve urban infrastructures and reduce carbon footprint. Accurate and continuous information about traffic is necessary to implement effectively such decisions. This is not always possible because of the cost of the information: it is not possible to install sensor devices at large scale because of financial costs and privacy; employing a plethora of sensors requires significant computational capabilities to process the generated data. A centralized data analysis can hinder real-time applications, and limit their practical deployment in traffic management systems. This paper introduces a novel privacy-aware method for estimating traffic density using edge computing and without over-deploying privacy-intrusive surveillance technologies such as cameras. The objective is to reduce the cost of collecting data while providing accurate information to support traffic operators in decision making. We evaluate the proposed solution using a realistic traffic data of Bologna in Italy. Results shows that it yields a 45% lower average estimation error compared to standard prediction methods. Virtual traffic monitoring devices are associated with software agents that collect data from simulated traffic and estimate traffic density measurements when this information is not available. In our experiments, when we replace 50% of camera devices with cooperative low-cost edge devices, we obtain an average percentage error of just 22%. This result indicates that the cooperation between virtual traffic monitoring devices offers a means to avoid massive deployment of camera surveillance devices using low-cost information provided by connected vehicles. We also compared the results to those obtained by standard regression techniques.https://ieeexplore.ieee.org/document/10360829/Smart citytraffic monitoringmulti-agent systemsmissing information estimationInternet of Thingsurban sensing |
| spellingShingle | Davide Andrea Guastella Evangelos Pournaras Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance IEEE Access Smart city traffic monitoring multi-agent systems missing information estimation Internet of Things urban sensing |
| title | Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance |
| title_full | Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance |
| title_fullStr | Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance |
| title_full_unstemmed | Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance |
| title_short | Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance |
| title_sort | cooperative multi agent traffic monitoring can reduce camera surveillance |
| topic | Smart city traffic monitoring multi-agent systems missing information estimation Internet of Things urban sensing |
| url | https://ieeexplore.ieee.org/document/10360829/ |
| work_keys_str_mv | AT davideandreaguastella cooperativemultiagenttrafficmonitoringcanreducecamerasurveillance AT evangelospournaras cooperativemultiagenttrafficmonitoringcanreducecamerasurveillance |