# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Transport Systems & Traffic Flow
#### 3.1.3 Improvement in Existing Traffic Forecasting Studies  
Continuous improvement in traffic forecasting is crucial for adapting to the ever-changing dynamics of urban traffic. Enhancing existing forecasting models ensures that they remain effective and relevant in providing accurate predictions, which are critical for efficient traffic management. One can observe the need to refine and upgrade traffic forecasting models to incorporate new data sources and advanced machine learning techniques. Researchers have been working on improving the accuracy and reliability of these models to better serve the needs of smart cities. By highlighting this subtheme, researchers can systematically evaluate and enhance the performance of current models. It encourages a continuous feedback loop where models are regularly tested, validated, and updated to incorporate the latest advancements in machine learning and data analytics. In considering the threat landscape within transportation flow and networks, the backdrop highlights the vulnerability of in-vehicle communications to cyber-attacks, posing potential risks to overall communication structures and human safety. The absence of robust security mechanisms for vehicles is identified as a potential source of chaos in urban settings. This subsection delves into the exploration of cybersecurity measures in traffic, highlighting the imperative of Intrusion Detection Systems (IDS) and the utilization of machine learning (ML) techniques for anomaly detection. Various works in the field have proposed IDS solutions using ML algorithms, addressing a spectrum of cyber threats. [PERSON] et al. (2023), however, critically note the oversight in many of these works regarding non-trivial ML issues such as data distribution and class balance, tending to neglect simpler and more interpretable ML models [55]. The research employs simulated scenarios in a virtual machine using the NS-3 network simulator, containing different maps of Lisbon, Portugal, generated with SUMO (Simulation of Urban Mobility). Datasets include crucial information like simulation time, packet transmission/reception tags, identifier, and packet size. The article stresses the importance of data diversity in training sets for accurately classifying flooding behavior. Notably, the results highlight the effectiveness of training with multiple datasets compared to a single dataset. In comparison with similar works, the proposed approach demonstrates superior results in terms of F1 scores despite employing simpler ML methods. The conclusion presents the significance of generating vehicular network datasets through simulations and the importance of data diversity for training ML algorithms to detect malicious behaviors, particularly flooding attacks in 5G-enabled vehicular networks [55]. Addressing the challenge of reliable test data for network intrusion detection and prevention systems, the article introduces supervised machine learning techniques, specifically decision tree, random forest, and XGBoost algorithms. Performance indices such as accuracy, precision, and recall are meticulously calculated for these algorithms, revealing high precision and recall rates. The paper written by [PERSON] et al. (2023) also acknowledges skepticism and a call for improvement in existing traffic forecasting studies [47]. Highlighting limitations, it proposes a novel approach using a sliding window technique to capture time-dependent features, integrating both machine learning and deep learning models for traffic prediction. Evaluation metrics include RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R\({}^{2}\) (R squared), with the LSTM (long short-term memory) model identified as the best-performing in terms of accuracy. The proposed method is validated by applying it to a different dataset, showcasing its potential for broader applications [47]. Results from different articles in the PRISMA review indicate that accurate prediction models significantly enhance transportation system efficiency in smart cities.