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Calibration and Validation of Psychophysical Car-Following Model Using Driver’s Action Points and Perception Thresholds
This study develops a method of calibrating and validating the Wiedemann car-following model using vehicle trajectory data. Unlike sensitivity analysis and optimization, this method conforms to the assumptions of the original Wiedemann 99 model related to drivers’ car-following behavior. Eight calibration constants (CCs) of the model were estimated using the vehicle trajectory data from a section of the US-101 freeway in Los Angeles, California. (desired time gap from lead vehicle) and (maximum change in spacing) were determined from the observed maximum and minimum spacing between the lead and following vehicles with similar speeds. and (minimum relative velocity at which the driver starts decelerating and accelerating, respectively, with short spacing of the lead vehicle or so-called action points) and (effect of spacing on these action points) were determined using a segmented linear regression model. This model provided the estimated relative velocities at which the speed of a following vehicle changed in response to a lead vehicle using constant acceleration/deceleration. It was found that the absolute values of and were not the same, which indicates that drivers are more sensitive to lead vehicles in the closing process than the opening process. was calculated as the mean difference in constant accelerations of lead and following vehicles. was calculated as the mean acceleration of all vehicles 1 s after the vehicles increased from slow speeds (). Moreover, was calculated as the mean acceleration for speeds between 79.5 and . The traffic simulation with the estimated CCs in this study better reflected the observed speed distributions and action points than simulations with CCs estimated in previous studies using the same trajectory data.
Calibration and Validation of Psychophysical Car-Following Model Using Driver’s Action Points and Perception Thresholds
This study develops a method of calibrating and validating the Wiedemann car-following model using vehicle trajectory data. Unlike sensitivity analysis and optimization, this method conforms to the assumptions of the original Wiedemann 99 model related to drivers’ car-following behavior. Eight calibration constants (CCs) of the model were estimated using the vehicle trajectory data from a section of the US-101 freeway in Los Angeles, California. (desired time gap from lead vehicle) and (maximum change in spacing) were determined from the observed maximum and minimum spacing between the lead and following vehicles with similar speeds. and (minimum relative velocity at which the driver starts decelerating and accelerating, respectively, with short spacing of the lead vehicle or so-called action points) and (effect of spacing on these action points) were determined using a segmented linear regression model. This model provided the estimated relative velocities at which the speed of a following vehicle changed in response to a lead vehicle using constant acceleration/deceleration. It was found that the absolute values of and were not the same, which indicates that drivers are more sensitive to lead vehicles in the closing process than the opening process. was calculated as the mean difference in constant accelerations of lead and following vehicles. was calculated as the mean acceleration of all vehicles 1 s after the vehicles increased from slow speeds (). Moreover, was calculated as the mean acceleration for speeds between 79.5 and . The traffic simulation with the estimated CCs in this study better reflected the observed speed distributions and action points than simulations with CCs estimated in previous studies using the same trajectory data.
Calibration and Validation of Psychophysical Car-Following Model Using Driver’s Action Points and Perception Thresholds
Durrani, Umair (author) / Lee, Chris (author)
2019-07-10
Article (Journal)
Electronic Resource
Unknown
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