NONE 20260605092230341 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON SYSTEMS 1109-2777 2224-2678 Khadija Mouatassim Department of Computer Science and Mathematics, Chouaïb Doukkali University, ESTSB, ELITES Lab, El Jadida, MOROCCO Abdelfettah Mabrouk Department of Computer Science and Mathematics, Chouaïb Doukkali University, ESTSB, ELITES Lab, El Jadida, MOROCCO Chaimae Ouchicha Department of Management Assistance Techniques, Hassan 1st University (UH1), ENCG, FAMISDS Lab, Settat, MOROCCO Smart parking and traffic management are some of the major problems faced during the development of a smart city. There exist numerous challenges when trying to develop systems to solve the above-mentioned problem since Vehicular Ad-Hoc Networks (VANETs) produce a large volume of spatiotemporal data (vehicle speed, direction, and GPS position) in real-time. Nevertheless, due to their complexity, it is difficult to detect the existence of any abnormal events rapidly and reliably. In order to tackle such challenges, we suggest developing a hybrid deep neural network model, integrating Long-Short Term Memory (LSTM) network capable of capturing long-term dependencies and Squeeze-and-Excitation block (attention mechanism) highlighting important features. The proposed model is tested on AV-GPS-Dataset, containing GPS tracks of real vehicles as well as attack instances. Experiments show excellent results (accuracy equals to 99.81%, precision to 99.81%, recall to 99.73%, and F1 score to 99.74%), which outperform traditional methods significantly. Our results confirm that spatiotemporal data plays a vital role in solving problems stated. 06 05 2026 06 05 2026 393 31 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23202.2026.25.31 https://wseas.com/journals/systems/2026/a625102-013(2026).pdf Boukerche, A., Oliveira, H. A. B. 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