Many intelligent transportation systems (ITS) technologies generate massive amounts of operational data that are presently used primarily in real-time to affect traffic control strategies. These data offer great promise for uses beyond the execution of ITS control strategies, such as applications in transportation administration, policy, safety, planning, operations, and research. In most cases, ITS-generated data are voluminous in quantity and geographical and temporal coverage. ITS has the potential to provide data needed for planning, performance monitoring, program assessment, policy evaluation, and other transportation activities, including multimodal and intermodal applications. There is a broad spectrum of users who must rely on all available sources of data to feed the transportation planning and programming needs, modeling and simulation software, and finance/investment decisions. For these reasons (and others) ITS data, both historical and real-time should be capable of being either used immediately or stored, disseminated, and/or manipulated to support users. These data can include: (1) freeway data, (2) toll data, (3) arterial (nonfreeway) data, (4) parking management data, (5) transit and ridesharing data, (6) incident management data, (7) safety-related data, (8) commercial vehicle operations data, (9) environmental and weather data, (10) vehicle and passenger information data, and (11) intermodal operations data. For these reasons (and others), the objective of this paper is to explore the role of data in the future of ITS. This paper will examine several areas of ITS where data has become crucial: data for benefit/cost evaluation (i.e., "before data" versus "after data"); data for performance measurement, assessment, and auditing; data in support of programmatic activities (such as 511 implementation); and the business of data (location commerce, content providers, freight).
Samenvatting