摘要: |
One of the most important environmental factors that needs to be considered in determining various ATMS strategies is on-road tailpipe emissions. For a long time, MOBILE5A, a national standard emission factor model, has been used to generate emission rates for numerous air quality planning functions. It, however, is insensitive to a vehicle's speed profile and various acceleration/deceleration events. As such, the MOBILE5A model cannot be used to evaluate vehicle emission implications of alternative ATMS strategies, because in a real traffic network alternative ATMS strategies can substantially influence the changes of on-road vehicles' instantaneous speeds and acceleration rates. This paper presents the development of a series of new modal sensitive vehicle emission estimation equations for evaluating ATMS strategies. The emission data is collected for on-road driving conditions using a Remote Emission Sensor (RES), a cost-effective infrared technology designed to measure the levels of vehicle tailpipe emissions of hydrocarbon, carbon monoxide and oxide of nitrogen, and to simultaneously detect vehicles' instantaneous speeds and acceleration rates. With the collected on-road vehicle emission data, a vehicle's tailpipe emission rates are directly related to its speed profile and acceleration/deceleration events. Accordingly, the developed emission estimation equations can be effectively used to evaluate emission implications of various ATMS strategies. Using the new emission equations, the optimum driving speeds that will minimize emissions for various emission species and vehicle types can be determined based on a regular optimization logic. Thus, if the on-road drivers can be influenced, in some ways, to drive at these optimal speeds, the actual on-road vehicle emissions can be significantly reduced. The emission equations described in this paper are also used to emulate the standard FTP driving cycles, and the emission rates derived from MOBILE5A are thereafter compared with the emission rates for the on-road driving conditions. |