Multimodal urban mobility solutions for a smart campus using artificial neural networks for route determination and an algorithm for arrival time prediction

• Published in: Engineering applications of artificial intelligence Vol. 137; p. 109074
• Main Authors: dos Santos Sá, Joiner, do Nascimento da Silva, Edinho, Nunes Gonçalves, Leonardo, Mateus Machado Cardoso, Caio, Antloga do Nascimento, Andréia, Protásio dos Santos Cavalcante, Gervásio, Emília de Lima Tostes, Maria, Priscyla Leite de Araújo, Jasmine, José Brito Barros, Fabricio, de Souza Farias, Fabricio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Artificial intelligence; Artificial neural network; Multimodal mobility; Smart campus; Smart campus; Artificial neural network; Multimodal mobility; Artificial intelligence
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.109074

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.