Real time urban traffic prediction using RFID and a hybrid LSTM random forest model.

ABSTRACT: Traffic congestion poses a significant challenge to urban environments, impacting economic efficiency, safety, and quality of life. Traditional measures often fail to address the root causes of congestion, instead only delaying the inevitable symptoms of urban growth and increased vehicle use. This research proposes the Traffic Pattern Classification using LSTM-Random Forest (TPC-LSTM-RF), a novel prototype for real-time urban traffic management. The system uses Radio-Frequency Identification (RFID) technology for nonvisual vehicle tracking, ensuring privacy-by-design through on-the-fly anonymization, secure encrypted communication, and compliance with global data protection regulations. The system incorporates a novel hybrid machine learning algorithm, TPC-LSTM-RF, which combines temporal

SUBMITTER: Khattab O 

PROVIDER: S-EPMC12700983 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature