Abstract
For a follower-based service measure to be effectively used for 2-lane highway quality of service evaluation, a more reliable follower identification procedure is necessary. Current follower recognition methodologies are based on single critical headway values that may not accurately identify followers since they do not take into account the randomness of preferred tracking headways and drivers' desired speeds. This paper presents an improved, probability-based follower identification methodology that takes both of these factors into account while also considering their variability across different driving conditions. The calculated follower percentage and follower density values were compared with those estimated using the 3-second threshold suggested by the HCM. The proposed methodology estimated more followers, particularly during periods of heavy flow, highlighting the inability of using a single headway value to correctly identify followers. The resulting follower counts have been normalized, making it possible to theoretically identify followers at any given time or driving condition.
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