Using Acceleration Characteristics in Air Quality and Energy Consumption Analyses (SWUTC/96/465100-1)

This research investigated the effects of detailed speed and acceleration characteristics on energy consumption utilizing several fuel consumption models. The relationships between speed and acceleration characteristics, geometric characteristics (e.g., number of lanes, signal density, driveway density), and traffic flow variability for various roadways were also investigated. Finally, distributions were produced that summarize the operating characteristics of freeway and arterial street facilities in the Houston, Texas area. Data for the study were collected on a second-by-second basis on selected freeways and arterial streets in Houston, Texas using an electronic distance-measuring instrument (DMI) and the floating car technique.|The study found that fuel consumption models incorporating detailed speed and acceleration characteristics provide statistically different results. Similar results were obtained for both arterial and freeway roadways. Low coefficients of determination (i.e., R*R less than 0.35) were found when regressing geometric characteristics with the speed and acceleration characteristics such as average speed or acceleration noise. Relationships between the coefficient of variation of speed or acceleration noise with average speed provided much higher R*R values when investigating the traffic flow variability of the travel time runs. These results were similar for peak and off-peak conditions and the different roadway classifications (e.g., arterials and freeways).|The distributions of operating characteristics for Houston, Texas summarize the percent of time vehicles are operating within a given speed and acceleration range. This data is expected to be invaluable for individuals desiring the operational characteristics of the Houston roadway system, or similar large urban area, as well as those individuals who can apply this information to future or current mobile source emissions and energy consumption modeling applications.