摘要: |
It is believed that there exists a direct correlation between transportation volume and the amount of air pollutants emitted into the environment. A combination of connected vehicles (CVs) and environmental sensors to collect and monitor traffic flow and air pollutants at strategically selected schools in and around the state of South Carolina. Using connected systems (e.g., CVs, drones) fitted with sensors, periodic real-time traffic volume information and their respective emitted pollutants will be collected. Partial observations from connected vehicles will be utilized for real time emissions for better vehicle management at schools (less hazardous), particularly during drop-off and pickup times. Moreover, the data will be used for model development that can be used as forecasting tool. Different traffic parameters and their impact on emissions at school zones can be used for the optimization of land use and planning purposes. For instance, planning a school with a certain number of students would generate a volume of traffic in a zone that would result an amount of emissions based on vehicle types/compositions and locational specifications. This function can easily be minimized based some constraints and serve as decision-making tool. This research proposes to model and monitor real-time vehicle emissions, and develop better control schemes in the context of �low emission zones� at school zones. Real-time data from connected systems can as well utilized to improve the user decision making for environmentally friendly modes as well as fuel efficiency and better mobility with unnecessary stops. Impact and quantification of changes (due to daily, weekly or seasonal variations in commuter, freight, inclement weather, and incidents) in emissions will be addressed using rural roadways such as Orangeburg or I-85 near Clemson, SC congestion. |