摘要: |
Intelligent transportation systems (ITS) infrastructure automatically records vast amounts of traffi c data, which is highly useful for a variety of applications if properly archived. Induction loops are still the most common, although newer technologies continue to improve and have been successfully deployed. Since these devices are automated, this data is typically collected continuously and at a relatively fi ne temporal resolution.It is easy to fi nd applications for a data set of this sort, especially in regions where spatial coverage is high. A common use is in operational studies, such as before-and-after evaluation of traffi c management strategies. More recently, it has been suggested that transportation planners can use ITS data sets to assist in generating annual average daily traffi c (AADT) counts. Many other applications exist for calibrating planning models, validating mode choice and route choice models, and evaluating work zone channelization plans, to name just three.Four prime advantages of using ITS data for this purpose are increased coverage, more accurate statistical inferences, diminished safety risks to agency personnel collecting manual data, and the elimination of ineffi cient double counting of traffi c volumes by personnel in different agency departments. For these reasons, the Texas Department of Transportation (TxDOT) is interested in the feasibility of archiving and sharing ITS data. |