| Résumé : |
(auteur) We worked on one of the most significant research directions in Smart City, i.e., Intelligent Transportation System (ITS). ITS encapsulates several domains, such as electronic vehicles notification systems, traffic information, smart parking, and environment. However, in this thesis, we target two of its important domains; i) Smart Parking, and ii) Road Traffic. We started our research with Smart Parking use case. Performing literature review, we realized that different Machine Learning (ML) and Deep Learning (DL) approaches have been used for smart parking solutions. In most of these proposed approaches, enclosed parking areas were targeted with different feature sets to predict the "occupancy rate" in parking areas. It inspired us to conduct a comparative analysis to answer following questions; Given the parking prediction use case, how do the traditional ML models perform as compared to complex DL models? Provided big data, can less complex, traditional ML models outperform complex DL models? How well these models can perform to predict the availability of the individual on-street parking spots rather than predicting the overall occupancy rate of an enclosed parking area. To answer these questions, we choose five well-known classical ML algorithms (K-Nearest Neighbours, Random Forest, Decision Tree) and DL algorithm (Multilayer Perceptron). To take our investigation into depth, we train Ensemble Learning Model, in which we combine all the above-mentioned ML and DL models. A huge parking dataset of city of Santander, Spain, has been used which consists of around 25 million records. We also propose to recommend available parking spots based on the current location of the driver. Moving forward with our research goals, we performed literature review on road traffic and found road traffic associated with air pollution and noise pollution often. However, to the best of our knowledge, air pollution & noise pollution have never been use d in traffic prediction problem. In this part of our research, firstly we used air pollution (CO, NO, NO2, NOx, and O3) along with the atmospheric variables, such as wind speed, wind direction, temperature, and pressure to improve the traffic forecasting in the city of Madrid. This successful experiment motivated us to extend our investigation to another factor, which is also strongly correlated with road traffic i.e., noise pollution. Hence, as an extension of our previous work, in this part of our research, we use noise pollution to improve the traffic prediction in the city of Madrid. |
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesTowards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)
