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Report #6 in the series: Access to Destinations Study. This research aids in tackling one important part of accessibility metrics-measuring land use. It introduces complementary strategies to effectively measure a variety of different destination types at a highly detailed scale of resolution using secondary data. The research describes ways to overcome common data hurdles and demonstrates how existing data in one metropolitan area in the U.S.-the Twin Cities of Minneapolis and St. Paul -can be exploited to aid in measuring accessibility at an extremely fine unit of analysis (i.e., the parcel). Establishment-level data containing attribute information on location, sales, employees, and industry classification was purchased from Dun & Bradstreet, Inc. The research process involved cleaning and tailoring the parcel dataset for the 7-county metro area and integrating various GIS datasets with other secondary data sources. These data were merged with parcel-level land use data from the Metropolitan Council. The establishment-level data were then recoded into destination categories using the 2 to 6-digit classifications of the North American Industry Classification System (NAICS). The development of important components of this research is illustrated with a sample application. The report concludes by describing how such data could be used in calculating more robust measures of accessibility.

The functioning of the system of land use and travel networks in a region can be encapsulated into measures of the ease of reaching destinations from various locations, often referred to as accessibility measures. Regardless of the form used to specify accessibility, all measures require as inputs travel times between the zones of a region. For most transportation planning purposes, these travel time calculations are limited to motorized modes (auto and public transit), since these modes carry the bulk of all urban travel. In this research study, attention is focused on developing methods for calculating travel times by non-auto modes, including walking, bicycling and public transit. Unique networks for each mode are developed, accounting for the presence of special facilities such as pedestrian or bicycle trails and on-street bike lanes. A statistical model is estimated to identify the influence of special bicycle facilities on travel speeds, using GPS data collected from bicyclists in a real-world setting. These methods are demonstrated with an application to a section of the Twin Cities metropolitan region encompassing parts of the cities of Minneapolis, St. Paul and Bloomington. The output of the application of these methods are a set of maps depicting travel sheds from various locations within the study area. The data are displayed for three points in time: 1995, 2000 and 2005. Changes to these travel sheds over time are demonstrated with maps that show the difference in travel time between each set of origins and destinations for each pair of years. The research concludes with some suggestions about the uses of the travel time data, such as the calculation of multimodal, multipurpose measures of accessibility.

The widespread implementation of automated vehicle location systems and automatic passenger counters in the transit industry has opened new venues in transit operations and system monitoring. Metro Transit, the primary transit agency in the Twin Cities, Minnesota region, has been testing various intelligent transportation systems (ITS) since 1999. In 2005, they fully implemented an AVL system and partially implemented an APC system. To date, however, there has been little effort to employ such data to evaluate different aspects of performance. This research capitalizes on the availability of such data to better assess performance issues of one particular route in the Metro Transit system. We employ the archived data from the location systems of buses running on an example cross-town route to conduct a microscopic analysis to understand reasons for performance and reliability issues. We generate a series of analytical models to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level. The methodology includes multiple approaches to display ITS data within a GIS environment to allow visual identification of problem areas along routes. The methodology also uses statistical models generated at the time point segment and bus route level of analysis to demonstrate ways of identifying reliability issues and what causes them. The analytical models show that while headways are being maintained, schedule revisions are needed to in order to improve run time. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends consolidation them.