In order to validate this approach, the researchers used data from the Katy and NTE freeways in Texas. Both routes have MLs that usually operated at or near free-flow speeds and GPLs that were routinely congested at peak hours. The data were collected from Automatic Vehicle Identification (AVI) sensors operated by the Texas DOT along the freeways, and were able to detect vehicles' unique transponder IDs to allow drivers' choice of roadway on a granular basis. The dataset contained nearly two million unique travelers making over 24 million trips. The authors note that this is significantly larger than most previous studies on travelers' revealed preference (RP).
The data suggested that the frequency of a traveler's freeway use had only marginal impact on their choice of lane. With the exception of travelers who made very few trips per month (between 1 and 3), drivers tended to exhibit similar rates of ML usage on each road. Additionally, it was found that frequent commuters typically received the same value for toll dollars as those who traveled infrequently. This indicates that commuters are not significantly more adept at judging the cost-benefit ratio of a ML toll than other drivers. Finally, a random sample of individual driver profiles showed that the ML's time savings and toll price had almost no impact whatsoever on a traveler's decision to use a ML.
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