Publication Detail
Modal Activity Under Varying Traffic Conditions on California Freeways Experimental Design and Data Collection Procedures
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UCD-ITS-RP-96-20 Presentation Series Download PDF |
Suggested Citation:
Young, Troy M., Simon P. Washington, J. L. Botha, Daniel Sperling (1996) Modal Activity Under Varying Traffic Conditions on California Freeways Experimental Design and Data Collection Procedures. Institute of Transportation Studies, University of California, Davis, Presentation Series UCD-ITS-RP-96-20
Proceeding TRB Transportation and Air Quality Committee Summer Conference, Irvine, CA
The current development of emission models is heading in a direction that will create a gap between regional transportation activity models and regional emission models. New generation emission models are being developed to predict emissions as a function of particular modes of vehicle operation. Hence, there is a need to forecast these modes of operation; namely accelerations, decelerations, cruises and idle periods.
This paper describes a project, funded by the California Department of Transportation (Caltrans), that aims to develop a method for collecting data and deriving speed-time profiles that represent 'typical' driving on different facilities and under varying traffic conditions. Such profiles, or driving cycles, will bridge the gap between current travel demand models and emerging modal emission models. The findings of this research will help to merge macroscopic outputs from travel demand models (traffic volumes and average speeds) and microscopic-level inputs required by modal emission models.
An initial data collection effort has been completed and the project team is in the process of analyzing data from three corresponding sources: loop detectors, instrumented vehicles, and video footage collected from helicopter overflights. This data collection effort was carried out on the Route 101 freeway in Marin County, San Francisco Bay Area. The loop detector data provide an estimate of facility traffic volumes and speeds (level of service), while instantaneous speeds and accelerations from the instrumented vehicles provide estimates of modal activity distribution by lane, time of day (traffic conditions), and driver characteristics. Video data were collected to estimate and compare speed-time profiles between the instrumented vehicle and surrounding vehicles.
This paper focuses on the data collection procedures, and some of the lessons learned during the exercise.
The current development of emission models is heading in a direction that will create a gap between regional transportation activity models and regional emission models. New generation emission models are being developed to predict emissions as a function of particular modes of vehicle operation. Hence, there is a need to forecast these modes of operation; namely accelerations, decelerations, cruises and idle periods.
This paper describes a project, funded by the California Department of Transportation (Caltrans), that aims to develop a method for collecting data and deriving speed-time profiles that represent 'typical' driving on different facilities and under varying traffic conditions. Such profiles, or driving cycles, will bridge the gap between current travel demand models and emerging modal emission models. The findings of this research will help to merge macroscopic outputs from travel demand models (traffic volumes and average speeds) and microscopic-level inputs required by modal emission models.
An initial data collection effort has been completed and the project team is in the process of analyzing data from three corresponding sources: loop detectors, instrumented vehicles, and video footage collected from helicopter overflights. This data collection effort was carried out on the Route 101 freeway in Marin County, San Francisco Bay Area. The loop detector data provide an estimate of facility traffic volumes and speeds (level of service), while instantaneous speeds and accelerations from the instrumented vehicles provide estimates of modal activity distribution by lane, time of day (traffic conditions), and driver characteristics. Video data were collected to estimate and compare speed-time profiles between the instrumented vehicle and surrounding vehicles.
This paper focuses on the data collection procedures, and some of the lessons learned during the exercise.