# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Transport Systems & Traffic Flow
#### 3.1.2 Traffic Congestion  
Urban traffic congestion is a major problem, resulting in longer travel times, higher fuel consumption, and increased pollution. Effective congestion management is vital for enhancing the quality of life in smart cities. Machine learning techniques can analyze patterns and predict congestion, enabling proactive measures to mitigate traffic jams. Studies have shown that machine learning models can effectively predict congestion patterns by analyzing historical traffic data and real-time inputs from various sensors and devices. Focusing on traffic congestion as a subtheme allows researchers to delve into specific strategies for congestion prediction and management. This includes developing algorithms that can optimize traffic signal timings, suggest alternative routes, and manage traffic in real time to alleviate congestion.  
In the context of addressing traffic congestion in large urban cities, a significant factor is the quest for parking spaces. This subsection delves into the intricacies of traffic contingency and parking issues, aligning with the broader theme of traffic flow. One paper aligning with this theme introduces a framework utilizing diverse machine learning models to predict long-term parking space occupancy specifically in Los Angeles. The study meticulously evaluates over 10 machine learning models, incorporating both parking data and external factors like meteorological data. The primary objective is to enhance prediction accuracy and subsequently improve traffic flow within urban areas, as emphasized by [PERSON] et al. (2022) [39].  
Building upon previous work that predominantly focused on short-term predictions and limited parking lots, other papers emphasize the efficiency of various machine learning models in tackling urban mobility challenges. The need to predict parking space availability is underscored, and the integration of multisource data and deep learning techniques is explored for reducing traffic congestion [29]. Furthermore, the integration of Federated Learning with Machine Learning Algorithms (FL-MLA) is discussed as a key element in reducing traffic congestion and enhancing travel safety [50].  
To achieve efficient traffic flow and road safety, low-latency communication between vehicles and Roadside Units is emphasized. Machine learning algorithms are proposed to address safety, communication, and traffic issues associated with Vehicular Ad Hoc Networks (VANETs). The article also explores the combination of meta-heuristics methods with machine learning techniques for IoT-based smart monitoring applications.  
A multi-stage prediction approach using attention-based convolution neural networks with long short-term memory (CNN-LSTM) is also discussed for enhancing energy consumption, traffic estimation, parking occupancy, efficiency ratio, and accident detection. The integration of FL-MLA in traffic management is highlighted for its potential to significantly reduce traffic congestion. Therefore, the use of fuzzy logic control systems in traffic management and smart parking emphasizes the role of expert knowledge in decision-making. It also explores the application of fuzzy models in traffic light management systems to minimize waiting times at red lights. The use of artificial neural networks (ANN) is presented to forecast traffic flow, especially in adverse weather conditions [25; 53].