A New Approach to Estimate Vehicle Emissions Using Inductive Loop Detector Data

作者：Jeng Shin Ting^*; Nesamani K S; Ritchie Stephen G

来源：Journal of Intelligent Transportation Systems: Technology, Planning, and Operations , 2013, 17(3): 179-190.

DOI：10.1080/15472450.2012.712495

• Emissions
• Inductive Loop Detector
• MOVES
• Vehicle Classification
• Vehicle Reidentification

Motor vehicles are significant contributors to urban air pollution and greenhouse gases. Common practice for estimating vehicle emissions in California calls for integrating travel forecasting models and emission models. However, static travel forecasting models are incapable of generating the detailed vehicle activity required for emission estimates. Further, the fleet mix is also assumed to be constant across different roadways and at all times of day. Therefore, this article attempts to develop a new approach to measure travel activity and vehicle mix using existing inductive loop detector data. However, this study does not intend to forecast future vehicle activity. The study found that current practices overestimate speeds as much as 5-25 mph, whereas the proposed method overestimates speed about 2 mph, compared to ground-truth speeds in a freeway corridor. Furthermore, contrary to current practice, the proposed model distinguishes the vehicle miles traveled (VMT) between light-duty vehicles and heavy-duty vehicles in each link. The current practice overestimates or underestimates emissions by 1-20% during different times of day, whereas the proposed method underestimates the emissions by about 3%. We conclude that the proposed approach can provide a cost-effective way of estimating reliable emission inventory and estimating time-dependent emission inventories for different pollutants.

• 出版日期2013