Deriving performance measures for transportation planning using intelligent transportation systems archived data

Various modern sensor technologies deployed under the auspices of intelligent transportation systems (ITS) for data collection and archiving have helped in accumulating a wealth of transportation data in the form of data archives. Archiving of transportation data obtained from intelligent sources is practiced in most parts of the US under the auspices of the states' departments of transportation. However, recently there is a shift in the focus of archived data management systems (ADMS) from data collection and archiving to data analysis and distribution to stakeholders. This article discusses the use of archived ITS data for the development of performance measures for transportation planning and air quality support service. This service is packaged within a larger ADMS effort called Traffic Management Centers (TMC) Applications of Archived Data, which is also known by its working title ‘ADMS Virginia’. Nine sub-services for computing various performance measures at different spatial and temporal levels of aggregation are available within the transportation planning and air quality service. The nine performance measures provided are traffic speed, volume, density, vehicle miles traveled (VMT), percent VMT by time of day, travel time, volume-to-capacity ratio, peak hour factor, and average daily traffic (ADT). The service integrates a subset of a regional transportation planning network with the traffic flow data in the archived databases. The performance measures developed in this study have a broad spectrum of uses ranging from long- and short-range transportation planning, transportation system monitoring, regional air quality monitoring and air quality conformity, development of forecasting and simulation models, and establishment of growth impact policies. The concepts of operations of this system are discussed along with functional requirements, data model, and algorithms for deriving the performance measures. The methodology and procedures discussed in this article are portable and can easily be adopted by other ADMS efforts.