Smart City Gnosys
Smart city article details
| Title | Urban Crowdsensing By Personal Mobility Vehicles To Manage Air Pollution |
|---|---|
| ID_Doc | 59890 |
| Authors | Jiménez P.; Santa J.; Rubio-Aparicio J.; Ramos E.; Martínez R.; Egea-López E. |
| Year | 2023 |
| Published | Transportation Research Procedia, 71 |
| DOI | http://dx.doi.org/10.1016/j.trpro.2023.11.071 |
| Abstract | The digitalization of cities and the development of smart, green, and integrated transport are societal challenges to transform cities into places with good quality of life now and in the future. The Internet of Things (IoT) comes with new advances to connect a multitude of sensing devices and even actuators, and they are presenting the cornerstone of Smart City deployments worldwide. So far, these advances have focused on static sensors in scenarios such as gardens, smart lighting, climate monitoring, or traffic management. However, moving sensors could rise the monitoring capabilities of smart cities to the next level, helping to better reflect the status of large areas without replicating fixed stations. This work proposes taking advantage of urban vehicles and, especially, personal mobility vehicles (PMVs), to implement such a perspective. Hence, a low-cost and energy-aware onboard unit (OBU) is designed to gather environmental data and support sustainable mobility applications. This on-board platform is provided with Low-Power Wide Area Network (LPWAN) communication technologies, enabling an Internet connection following an IoT scheme. The unit is equipped with sensors to measure air pollution in terms of NO2, CO, SO2, O3 and PMx, noise, and weather parameters. While moving across the city, PMVs mounting this device can collect data in a crowdsensing scheme. This data feed is complemented by a set of wireless traffic sensors, and they are subject to intelligent processing to monitor pollution and mobility parameters. For this, a back-end software module is powered with temporal series analysis to generate predictions based on tendencies detected in both pollution and mobility values. A front-end Web application has been implemented to show all past, current, and predicted data, offering functionalities to monitor urban mobility, minimize travel times, detect pollution areas, and recommend healthy routes across streets with low contamination levels. © 2023 Elsevier B.V.. All rights reserved. |
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