Applying a Multilayer Perceptron for Traffic Flow Prediction to Empower a Smart Ecosystem

Abstract

A direct impact of population density is more cities suffering from constant traffic jams. Thinking this way, Intelligent Transportation Systems, a key area in smart cities, uses computational intelligence techniques and analyses to aid in traffic dimensioning solutions. In this context, accurate traffic prediction models are vital to creating a more autonomous and intelligent environment. With an increase in projects for intelligent cities, research in the area of computational intelligence becomes a necessity, since its models can address complex real-world problems, which are usually difficult for conventional methods. In this work, an application is introduced applying machine learning to empower a smart ecosystem. To validate it, an extensive evaluation was performed, comparing it with the state-of-the-art and, also, verifying the impact of parameter variation and activation functions on the model of traffic flow prediction. All evaluations were done using real data traffic of two very distinct scenarios. Firstly, a free traffic flow scenario was evaluated in a benchmark dataset. Then, both models were evaluated in a complex traffic scenario where traffic flow is not continuous nor large. In both scenarios, the presented application, called SmartTraffic, outperforms the current state-of-the-art, with a performance gain of over 100% when compared in the first scenario and an improvement of approximately 31%, on average, in the second one.