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Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: a Smart Home Use Case

Lee, G (2023) Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: a Smart Home Use Case. Sensors, 23. ISSN 1424-8220

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Open Access URL: https://doi.org/10.3390/s23042174 (Published version)


This paper defines a smart home use case to adjust home temperature automatically. The main objective is to reduce energy consumption of the cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for heating, cooling and/or hot water. When the residences leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving the residences, the cooling or heating devices start working automatically to adjust the home temperature according to the residence desire (i.e., X degree Celsius). It can help to reduce energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this work uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residences behaviour inside homes, this work focuses on predicting residences behaviour outside home (i.e., arrival time as a context) to reduce energy consumption of smart homes. One main objective of this work is to find the most appropriate Machine Learning and Neural Network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can be also used as a route recommender to offer the quickest route for drivers.

Item Type: Article
Uncontrolled Keywords: 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Computer Science & Mathematics
Publisher: MDPI
SWORD Depositor: A Symplectic
Date Deposited: 07 Feb 2023 12:38
Last Modified: 21 Feb 2023 10:00
DOI or ID number: 10.3390/s23042174
URI: https://researchonline.ljmu.ac.uk/id/eprint/18817
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