Lauren P. Alexander

Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin–destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin–destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.

Transit Cooperative Research Program (TCRP) project H-39, “Methodology for Determining the Economic Development Impacts of Transit Projects,” was aimed at developing a method for transit agencies to assess whether and under what circumstances transit investments have economic benefits that are in addition to land development stimulated by travel time savings. It addresses the productivity increases associated with agglomeration economies—economies of scale in density—that may be caused by transit improvements. The authors reviewed existing evaluation practices and academic research, and then carried out a wide-ranging empirical study on metropolitan-level data from cities across the U.S., firm-level data from two metropolitan areas, and case studies of three recent transit projects. A spreadsheet tool was produced that could be used by transit agencies and others to estimate the agglomeration-related economic benefits of rail investments in the forms of new systems or additions to existing systems. With information about any of five possible measures of the proposed investment—track miles, total seating capacity, rail-specific seating capacity, rail revenue miles, or total revenue miles—the spreadsheet provides a range of possible wage or GDP impacts. The tool is best used to compare the agglomeration impacts of transit investments in different metropolitan areas to each other.