# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Transport Systems & Traffic Flow
#### 3.1.1 Vehicle Speeds  
Accurately predicting vehicle speeds is essential for developing efficient traffic management systems. This involves creating models that can forecast the speed of vehicles in various conditions, which is critical for optimizing traffic flow and minimizing delays. Accurate speed predictions contribute to smoother traffic operations, reduce fuel consumption, and decrease emissions. The models that focus on the need for precise and reliable speed prediction models often employ advanced machine learning techniques to handle the complexity of urban traffic environments [5]. The ability to predict vehicle speeds helps in implementing real-time adjustments to traffic signals and managing speed limits dynamically, thereby improving overall traffic efficiency. By isolating vehicle speeds as a subtheme, researchers can specifically address the unique challenges and technological requirements needed to improve speed prediction models. This focused approach allows for the development of specialized algorithms and tools tailored to enhance vehicle speed forecasting. One example is the development and application of machine learning models to predict vehicle speeds in Cauayan City, Philippines. This accentuates the challenges faced by developing countries in implementing smart city initiatives. The study utilizes gradient-boosting tree models, incorporating road properties, day and time, and land use data as features. The machine learning model aims to predict a 1-min average probe vehicle speed based on various factors, including land use patterns, road properties, day-of-week, and hour-of-day features [32]. The findings of the study not only provide valuable insights for urban planners regarding impactful predictors of traffic behavior but also underline the significance of considering road features, land use, and time-related factors in effectively modelling vehicle speeds in developing cities. Machine learning algorithms also play a pivotal role in predicting traffic flow patterns based on historical and real-time data, weather conditions, and other influencing factors, thereby enhancing urban transportation management [5]. One study focuses on the utilization of machine learning strategies, including decision trees, random forests, extra trees, and XGBoost (2.0.0. ), to classify Internet of Vehicles (IOVs) traffic in the road system. This research analyzes the link between the characteristics of the machine learning dataset and its accuracy in classifying real-world IOV traffic. The study also proposes an Intelligent Intrusion Detection System (IDS) for IOV-dependent Vehicle-to-Network Communication (VNT) using the tree-based machine learning technique KNN (K-Nearest Neighbours). Therefore, the study emphasizes the effectiveness of tree-based machine learning models in classifying IOV traffic and intrusion detection in smart cities [31]. Another study introduces a lightweight machine learning-based data collection protocol named ML-TDG to address the challenges of data collection and communication in real-time traffic environments with millions of vehicles. This protocol utilizes real-time traffic data, distinguishes indexes based on assigned criteria, and collects data with minimal sources. The study focuses on the integration of Machine Learning into Vehicular Ad Hoc Networks (VANETs) and Intelligent Transport Systems (ITS) to enhance traffic management, data processing, and communication efficiency [26; 38]. Data from Tumakuru city service buses are also used for model development. The non-linear model employs extreme gradient boosting (XGBoost) to estimate BAT based on factors like land use patterns, signalized intersections, and section length. The proposed Dynamic BAT Estimation Model considers preceding trip data, estimating BAT for individual bus stops. Adjusted Travel Time is calculated as the weighted average of forecasted and estimated travel time.