A New Approach to Interoperability within the Smart City Based on Time Series-Embedded Adaptive Traffic Prediction Modelling

• DOI: 10.1007/s11067-024-09662-y
• Publication Date: 2024-11-25T08:33:30Z
• Authors: Victor Fernandez; Virgilio Pérez
• Keywords: Smart City

The rapid urbanization observed over recent decades has led to important challenges in urban mobility, notably traffic congestion, pollution, and inefficient energy consumption. Concurrently, the rise in electric vehicles (EVs) offers a promising shift towards sustainable urban transport yet introduces complexities such as the need for extensive charging infrastructure and effective energy demand management. This study addresses these challenges by proposing a predictive model for real-time and future traffic volume estimation, leveraging historical data, real-time information, scheduled city events, and the availability of EV charging infrastructure. The methodology proposed employs actuarial techniques to create a comprehensive framework that predicts traffic patterns and optimizes energy resources within smart cities. By integrating variables such as historical traffic patterns and real-time data, our model provides accurate traffic forecasts essential for urban planning and energy distribution. We utilize a time-series based algorithm to predict traffic, validated through real data from pilot projects in Ljubljana, Slovenia. The study's findings underscore the model's potential to enhance urban mobility and energy efficiency, providing a robust tool for city planners and policymakers.