학술논문

A PM2.5 concentration prediction framework with vehicle tracking system: From cause to effect
Document Type
Conference
Source
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Computing and Communication Technologies (RIVF), 2022 RIVF International Conference on. :714-719 Dec, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Correlation
Density measurement
Atmospheric measurements
Surveillance
Urban areas
Estimation
Traffic control
PM2.5 estimation
Air Pollution
Air pollution modeling
Computer Vision
Traffic Surveillance
Language
Abstract
Air pollution is an emerging problem that needs to be solved especially in developed and developing countries. In Vietnam, air pollution is also a concerning issue in big cities such as Hanoi and Ho Chi Minh cities where air pollution comes mostly from vehicles such as cars and motorbikes. In order to tackle the problem, the paper focuses on developing a solution that can estimate the emitted PM2.5 pollutants by counting the number of vehicles in the traffic. We first investigated among the recent object detection models and developed our own traffic surveillance system. The observed traffic density showed a similar trend to the measured PM2.5 with a certain lagging in time, suggesting a relation between traffic density and PM2.5. We further express this relationship with a mathematical model which can estimate the PM2.5 value based on the observed traffic density. The estimated result showed a great correlation with the measured PM2.5 plots in the urban area context.