Instantaneous Emission Modeling with GPS-Based Vehicle Activity Data: Results of Diesel Trucks for One-Day Trips

Abstract

This paper presents an instantaneous analysis for traffic emissions using GPS-based vehicle activity data. The different driving conditions, including real-time and average speed, short-time stops and long-time stops, acceleration and deceleration, etc., are extracted from GPS data. The hot emission, cold-start emission and idling emission, varied by nitrogen compounds and particulate matter are calculated, respectively, in terms of the driving condition and vehicle characteristics. Results simulated based on a one-day trip activity dataset show that trucks spend most kilometers on national roads, followed by municipal and provincial roads. The number of short-time stops is significantly higher than long-time stops, and the time spent for long-time stops is higher than short-time duration. The hot emission accounts for the largest proportion of emissions, and the idling emission also contribute substantially. Results of sensitivity analyses indicate that pollutions in urban area from freight transport can be significantly decreased by increasing the vehicle classes and guiding the heavy trucks out of the region.