Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity

Propuesta de un modelo predictivo para el tráfico aéreo de pasajeros en el Perú, 2023

En el presente trabajo de investigación, tuvo como objetivo proponer un modelo de predictivo para la toma de decisiones del tráfico aéreo de pasajeros en el Perú 2023. El tipo de investigación fue Aplicada y con diseño Preexperimental. La muestra poblacional fue un dataset del año 2019 al 2022. La metodología empleada para realizar el modelo predictivo fue CRIPS-DM, el lenguaje de programación fue PYTHON y el uso de librerías como Pandas, Numpy y Pycaret, etc., así como una base de datos en Excel. Como resultados se obtuvo: Para el Indicador 1 “Predicción del modelo predicativo” el mejor modelo que fue regresión de TheilSen; Para el Indicador 2 “determinar la métrica de regresión lineal” hubo un ajuste del modelo de 0.9965 y Para el Indicador 3 “determinar el error porcentual absoluto medio para establecer el desempeño” con un indicador de desempeño de 5.8791. Como conclusión general, en base a los tres (3) indicadores el mejor modelo que se ajustó para predecir el tráfico aéreo de pasajeros fue el modelo de regresión de TheilSen

Mobility management-based autonomous energy-aware framework using machine learning approach in dense mobile networks

A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%