improving parking availability prediction in smart cities with iot and ensemble based model

Abstract Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity

A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms

Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly, it helps real estate agents and investors make better investment decisions and negotiate contracts more effectively. In addition, housing prices are often an indication of the general state of the economy. A price decrease may indicate an economic recession, while an increase in prices may signal economic growth. In this study, we proposed to address this subject by predicting house prices using machine learning by choosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and GradientBoosting (GB). We tested our models on the Melbourne real estate dataset, which includes 34,857 property sales and 21 features

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al

A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms

Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly, it helps real estate agents and investors make better investment decisions and negotiate contracts more effectively. In addition, housing prices are often an indication of the general state of the economy. A price decrease may indicate an economic recession, while an increase in prices may signal economic growth. In this study, we proposed to address this subject by predicting house prices using machine learning by choosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and GradientBoosting (GB). We tested our models on the Melbourne real estate dataset, which includes 34,857 property sales and 21 features