Socioenvironmental assessment and application process for IOT: A comprehensive approach

Cavalieri, Adriane; Reis, João; Amorim, Marlene

doi:10.1016/j.jclepro.2023.140348

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Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2023.140348

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Socioenvironmental assessment and application process for IOT: A comprehensive approach

Adriane Cavalieri,

João Reis,

Marlene Amorim

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Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

Igorevich Rozhdestvenskiy,

Poornima

2024

MATEC Web Conf.

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This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.

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Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

Igorevich Rozhdestvenskiy,

Poornima

2024

MATEC Web Conf.

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Socioenvironmental assessment and application process for IOT: A comprehensive approach

Cited by 1 publication

References 69 publications

Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

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