Paper Details

TRB 89th Annual Meeting (January 10-14, 2010)

Event Number:	384
Event Title:	Vehicle-Highway Automation
Event Date:	Jan 11 2010 7:30PM- 9:30PM
Event Location:	Marriott, Salon 2
Event Description:
Event Agenda:

Energy and Emission Benefit Comparison of Stationary and In-Vehicle Advanced Driving Alert Systems (10-3829)

Automobiles powered by fossil fuels are one of the major contributors to both criteria pollutant and greenhouse gas, in particular carbon dioxide (CO2), emissions. Previous studies have revealed that unnecessary acceleration and hard braking in response to sudden changes of traffic signals may cause a significant amount of wasted energy and increased emissions. Altering drivers’ behavior when approaching signalized intersections could potentially reduce energy consumption and emissions of motor vehicles without increasing travel time/delay. This paper proposes two types of advanced driving alert systems (ADAS), i.e. stationary ADAS (based on roadside infrastructure such as changeable message sign (CMS)) and in-vehicle ADAS (driven by advanced communication technology such as vehicle-infrastructure integration (VII)), which can help drivers avoid hard braking at intersections by providing real-time traffic signal status information. Using a state-of-the-art modal emissions model, the impacts of these two types of ADAS on the reductions of vehicle fuel consumption and CO2 emissions are evaluated and compared. A numerical analysis of a single vehicle shows that ADAS can help reduce vehicle fuel consumption and CO2 emissions by up to 40% under hypothetical conditions tested. The benefits of these systems are further investigated in a traffic simulation environment under different levels of congestion and posted speed limits. The simulation results reveal that both CMS-based ADAS and VII-driven ADAS can provide fuel and CO2 savings, where the latter offers more savings in most cases.

Authors
     Wu, Guoyuan , University of California, Riverside
     Boriboonsomsin, Kanok , University of California, Riverside
     Zhang, Wei-Bin , University of California, Berkeley
     Li, Meng , Tsinghua University, China
     Barth, Matthew J., University of California, Riverside