Transportation Statistics and Databases

Complex Settings and Uses

Understanding transportation systems in their diverse modes, settings, and purposes is a complex task. Many data sets and statistical bodies are linked to a large number of different markets, demographic groups, and geographically distributed activities. Some of the transportation modes are operated in extremely competitive markets, while others are over-consolidated or monopolistic; some are highly regulated, others less so. Some are dominated by private operators, some by public, still others by hybrid forms of ownership and operation. Each of these scenarios and contexts tends to create statistical data of varying degrees of accuracy and accessibility.

Some transportation systems require great fixed capital assets and infrastructure. Others represent emergent modes or intermodal forms that will be altered in their effect and scope by virtue of their increasing connection to the existing networks [Page 1565]of separate technologies and their intelligent and opportunistic use of available data.

Among the kinds of data that provide inputs, or raw statistics, for database management systems to compile and sort are supply and demand, current and future needs, local and regional economic and demographic trends, monopolistic structures and competitive forces, fixed costs and variable prices. To make sense and practical use of this wealth of often counterweighted data, information management systems must not only take it in and sort it, but the type and scope of outputs also must be considered in terms of form, size, flexibility, searchability, and appropriateness to the policies in place and the tasks that need to be completed.

Another type of metric is also involved: the human factor. For utility in this area, a database must help its users grasp and interpret such “fuzzy” data as transportation customers' willingness to pay, their perceived value of service, and their perceived value of alternate or comparison services. Ideally, a database can handle humanistic input variables—such as lower travel cost or the seasonal desirability of particular destinations—in such a way as to generate output that can help transportation administrators and managers predict shifts in demand over the near and long term and plan accordingly.

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