Evaluating the relationship between the driver and roadway to address rural intersection safety using the SHRP 2 naturalistic driving study data.
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2016-02-01
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Abstract:Rural intersections account for 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly
understood safety problem. Transportation agencies have traditionally implemented countermeasures to address rural intersection
crashes but frequently do not understand the dynamic interaction between the driver and roadway and the driver factors leading to
these types of crashes.
The Second Strategic Highway Research Program (SHRP 2) conducted a large-scale naturalistic driving study (NDS) using
instrumented vehicles. The study has provided a significant amount of on-road driving data for a range of drivers. The present
study utilizes the SHRP 2 NDS data as well as SHRP 2 Roadway Information Database (RID) data to observe driver behavior at
rural intersections first hand using video, vehicle kinematics, and roadway data to determine how roadway, driver, environmental,
and vehicle factors interact to affect driver safety at rural intersections.
A model of driver braking behavior was developed using a dataset of vehicle activity traces for several rural stop-controlled
intersections. The model was developed using the point at which a driver reacts to the upcoming intersection by initiating braking
as its dependent variable, with the driver’s age, type and direction of turning movement, and countermeasure presence as
independent variables. Countermeasures such as on-pavement signing and overhead flashing beacons were found to increase the
braking point distance, a finding that provides insight into the countermeasures’ effect on safety at rural intersections. The results
of this model can lead to better roadway design, more informed selection of traffic control and countermeasures, and targeted
information that can inform policy decisions.
Additionally, a model of gap acceptance was attempted but was ultimately not developed due to the small size of the dataset.
However, a protocol for data reduction for a gap acceptance model was determined. This protocol can be utilized in future studies
to develop a gap acceptance model that would provide additional insight into the roadway, vehicle, environmental, and driver
factors that play a role in whether a driver accepts or rejects a gap.
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