1. ENERGY AND ENVIRONMENTAL IMPACTS OF ROUTE CHOICE DECISIONS Kyoungho Ahn and Hesham Rakha Virginia Tech Transportation Institute, VPI&SU, Blacksburg, VA 1. Abstract The paper investigates the impact of route choice decisions on vehicle energy consumption and emissions using fuel consumption and emission models with second-by-second floating-car GPS data. The study investigates two routes: a faster and longer highway route and a slower and shorter arterial route. The study demonstrates that the faster highway route choice is not always the best route from an environmental and energy consumption perspective. Specifically, the study demonstrates that significant improvements (savings of up to 63, 71, 45, and 20 percent in HC, CO, NOx, and CO2 emissions, respectively) to air quality can be achieved when motorists utilize a slower arterial route although they incur an additional 17 percent in travel time. Moreover, the study demonstrates that energy savings in the range of 23 percent can be achieved by traveling on the slower arterial route. The study also demonstrates that a small portion of the entire trip that involves high engine-load conditions has significant impacts on the total emissions, demonstrating that by minimizing high-emitting driving behavior air quality can be significantly improved. 3. Data Collection 4. Study Results 2. Introduction Motorists typically choose routes that minimize their travel time. Drivers may select longer routes if they produce savings in travel time. However, the question that needs addressing is whether taking a longer but faster route can result in energy and air quality improvements. This study investigates the impact of different route choices on vehicle fuel consumption and emission rates using GPS data gathered during the morning commute near a suburb of the Washington, DC metropolitan area. The objective of this study is to investigate the impact of two route choices on vehicle fuel consumption and emission rates. A number of research efforts have attempted to develop traffic assignment models that can enhance the environment. However, these research efforts have utilized simplified mathematical expressions to compute fuel and emission rates based on average link speeds without regarding transient changes in a vehicle’s speed and acceleration levels. To overcome the limitations of current research methods, this study adopted microscopic fuel consumption and emission models using instantaneous speed and acceleration levels. 4.1 Energy and Emission Models In order to estimate emission and fuel consumption using the second-by-second GPS probe vehicle data, the VT-Micro model, the Comprehensive Modal Emissions Model (CMEM), and the Environmental Protection Agency’s (EPA) MOBILE6 model were utilized. 4.2 GPS Data Analysis Estimated emissions and fuel consumptions on study corridors. 4.3 Case Study with Same Travel Time Emissions and fuel consumptions with same travel time Impacts of high engine-load conditions HighwayArterialDiff. Ave. Travel Time (min)25.6329.9-4.27 95 %tile of Travel time36.2537.86 5 %tile of Travel time23.3226.23 Std. Dev. of Trav. Time4.175.08-0.91 Average Speed (km/hr)85.4256.6228.8 Std. Deviation of Speed10.237.912.32 95 percentile of Speed94.1663.11 5 percentile of Speed59.2643.94 Number of Trips2118 5. Conclusions 3.2 GPS Data Collection A portable GPS unit, GD30L, manufactured by LAIPAC Technology Inc. was utilized in the study. The GPS unit is designed to record the date, time, vehicle longitude, vehicle latitude, vehicle speed, vehicle heading, and the number of tracking satellites. The GPS floating-car travel data were collected on weekdays (Monday through Friday) between March and May of 2006 using a test vehicle. The trip route (highway or arterial) was randomly selected on the day of data collection. In order to record the aggregate characteristics of traffic flow, the probe vehicle maintained the average speed of the traffic stream. 3.1 Study Corridor A morning commute GPS data were collected in the Northern Virginia area. The arterial route, VA Route 7, extends over 22.6 km (17.25 mi) and covers 32 signalized intersections while the highway route (35.85 km or 22.41 mi) connects two highway sections and two arterial sections with 8 signalized intersections. The study corridors are controlled by a signal system with an optimized cycle length of 180 seconds or 210 seconds depending on the time-of-the-day. Most of the signal cycle time is assigned to the main route, VA 7 and VA 28. The study utilizes GPS data collected under current traffic signal operations on the study sections during the morning commute period. HCCONOxCO2Fuel VT-Micro(ORNL) Highway Top 1 %16 %19 %4 %3 %4 % Top 2 %24 %30 %7 %6 %7 % Top 5 %39 %47 %17 %13 %14 % Top 10 %54 %64 %32 %23 %25 % VT-Micro(ORNL) Arterial Top 1 %15 %20 %5 %3 %4 % Top 2 %21 %29 %9 %6 %7 % Top 5 %34 %45 %21 %13 %14 % Top 10 %47 %60 %37 %24 %25 % CMEM24 Highway Top 1 %20 %38 %30 %3 %5 % Top 2 %32 %63 %50 %6 %9 % Top 5 %52 %80 %73 %14 %17 % Top 10 %81 %84 %90 %25 %28 % CMEM24 Arterial Top 1 %31 %63 %55 %4 %5 % Top 2 %47 %66 %64 %7 %8 % Top 5 %90 %71 %89 %15 %16 % Top 10 %100 %77 %100 %26 %27 % This study demonstrates that a UE or SO traffic assignment does not necessarily minimize vehicle fuel consumption and emissions based on second-by-second GPS commute field data. The study demonstrates that, for the specific example illustration, motorists could save 17 percent in travel time on highway travel over travel on an arterial route. However, significant improvements (savings up to 63, 71, 45, and 20 percent of emissions for HC, CO, NOX, and CO2, respectively) to air quality are observed when motorists utilize the slower arterial route. Moreover, the study found that 23 percent of energy cost can be saved when motorists sacrifice 17 percent of travel time by traveling on the arterial route. Finally, the study demonstrated that a small portion of the entire trip (10 percent) that involves operation at high engine loads can produce up to 50 percent of the total trip emissions and consume up to 25 percent of the total trip fuel consumption. Consequently, significant improvements in air quality and energy consumption are achievable by educating drivers.