Estimating Effects of Driver Age and Distraction on Freeway Operations and Safety Using NDS Data | Grant individual record
2016 - 2017
This project will develop and calibrate a model to examine the relationships between driver age and distraction on vehicle speeds, following distances, driver reaction times, and crash risks. Expected project tasks and outcomes are as follows (tentative dates also shown): (1) Literature Review. Summarize previous research on speed choice, vehicle-following behaviors, and crash risks, as these factors relate to driver age, driver distraction, and identify other potentially influential factors (e.g., congestion level, lane position, etc.). (October-November 2016). (2) Conceptualized Model. Determine relevant variables from the existing dataset and develop a conceptualized model for car-following behavior, the resulting operational conditions, and a qualitative assessment of the ramifications to safety. This step will use the input of senior researchers in the areas of human factors, operations, and safety. (Late October 2016). (3) Data Preparation. Inspect and clean National Data Service (NDS) data, prepare a database for analysis, and identify and match relevant Roadway Information Database (RID) data that could supplement the NDS dataset. (November-December 2016). (4) Calibrate Baseline Model. Calibrate the conceptualized model using data for drivers of all ages with no distractions and for events that did not result in a crash or near-crash; revise baseline model as appropriate. (January-February 2017). (5) Development of Expanded Model. Using the baseline model, develop an expanded model that incorporates distraction behaviors and crash/near-crash incidents. This expanded model will be used to study how crash risk correlates with driver age, driver distraction, and other variables identified as critical in previous steps. (February-March 2016). (6) Final Report/Paper. Document the outcomes of the research, including the effects of driver age, driver distraction, and other critical variables, and the degrees to which the interaction of these factors seem to impact speeds, following distances, reaction times, and crash risk. (April-May 2016). Data to be used: The research team will use the Freeway data obtained for the strategic project led by Avelar/Hammond last fiscal year. This dataset consists of 847 events from the NDS (including 82 near-crash events and 10 crashes). There are 105 potentially useful variables and up to 300 speed readings per event. Gap and headway data are available for multiple events as well. Advancement to the state-of-the-practice: This project will develop a quantitative framework characterizing the impact and interactions of driver age and distraction on operations and crash risk. The results are expected to: (1) provide formulations for realistic vehicle-following behavior, with potential for microsimulation implementation; (2) provide an estimation of crash risk given key operational conditions; (3) inform future research on older driver interactions with vehicle technologies; and (4) identify elements for improved strategies to driver outreach and education.