TY - GEN
T1 - Traffic accident monitoring system using radio frequency identification tools
AU - Pardo, Eduardo Martin Estela
AU - Chero, Jhonatan Axel Yataco
AU - Aguirre, Jimmy Armas
AU - Acosta, Alvaro Chavarri
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This article proposes a system that allows monitoring car accidents with the use of NFC technology. The solution is comprised of low-cost Radio Frequency Identification (RFID) tools integrated into a mobile and web application that interact with the Google Maps API for efficient monitoring. The car accident reporting process collects and sends data manually through different service channels, which generates delays and, in some cases, the receipt of erroneous data. The proposed solution automates the accident reporting process by storing data from the users involved in the RFID tags and displaying them in the mobile and web applications, when generating a new report. Also, our application interacts with the Google Maps API to show the exact location from where accidents are reported, in order to speed up the process of attention by the PNP. The validation was carried out in the city of Lima, Peru. Preliminary results yielded data showing a 94% reduction in the accident reporting process and a 94% reduction in the average data capture time by the National Police of Peru (PNP).
AB - This article proposes a system that allows monitoring car accidents with the use of NFC technology. The solution is comprised of low-cost Radio Frequency Identification (RFID) tools integrated into a mobile and web application that interact with the Google Maps API for efficient monitoring. The car accident reporting process collects and sends data manually through different service channels, which generates delays and, in some cases, the receipt of erroneous data. The proposed solution automates the accident reporting process by storing data from the users involved in the RFID tags and displaying them in the mobile and web applications, when generating a new report. Also, our application interacts with the Google Maps API to show the exact location from where accidents are reported, in order to speed up the process of attention by the PNP. The validation was carried out in the city of Lima, Peru. Preliminary results yielded data showing a 94% reduction in the accident reporting process and a 94% reduction in the average data capture time by the National Police of Peru (PNP).
KW - NFC
KW - RFID
KW - Surveillance
KW - Traffic accident
UR - https://www.scopus.com/pages/publications/85144150459
U2 - 10.1109/ICALTER57193.2022.9964798
DO - 10.1109/ICALTER57193.2022.9964798
M3 - Contribución a la conferencia
AN - SCOPUS:85144150459
T3 - Proceedings of the 2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2022
BT - Proceedings of the 2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2022
Y2 - 16 November 2022 through 19 November 2022
ER -
