El-Geneidy, Ahmed M.

Report #9 in the series: Access to Destinations Study. Conventional transportation planning is often focused on improving movement (or mobility) - most often by the automobile. To the extent that accessibility, a well-known concept in the transportation planning field since the 1950s, has been measured or used in transportation planning, such measures have also been auto-based. Broadening the scope of accessibility to include a wide array of destinations and non-auto modes such as walking, cycling, and transit has been previously proposed as a much needed aim among planning initiatives. A central issue is that to date, however, there have been few examples of measures draw from. When it comes to bicycling, walking, and transit measures of accessibility are an endeavor long on rhetoric but short on execution. This report discusses such hurdles, presents alternatives for overcoming them, and demonstrates how accessibility for walking, cycling, and transit - and for different types of destinations - can be reliably measured. We focus on explaining specific features of non-motorized transportation that complicate the development of accessibility measures, and offer solutions that conform to conventional transportation planning practice. In this research project, non-motorized measures of accessibility were developed for the entire seven counties of the Twin Cities (Minnesota, USA) metropolitan area. For purposes of this exposition in this report, we discuss the details of creating such measures using a sample application from Minneapolis, Minnesota, USA to demonstrate proof of concept for the endeavor.

Report #7 In The Series: Access to Destinations Study. This study presents an effort to track and model land use change in the Twin Cities Metropolitan Region. To that end, we make use of a unique, high-resolution, cell-level set of land use data for the Twin Cities. The data represent 75 meter by 75 meter land use cells, observed at several points in time during the period from 1958 to 2005. These data are used to validate three different types of land use models, which then are used to forecast land use several decades into the future. The models applied in this study include Markov Chain models, Markov Chain- Cellular Automata (MC-CA) models, and an empirical model based on a logistic regression specification. The models are intended to have a simple, transparent structure that allows the user to identify sources of forecast error. Forecasts of land use are made both for the entire study area and also for a specific corridor along State Highway 610 in the northwestern suburbs of the Twin Cities. The study concludes with a brief discussion of the limitations of the models, and how they might meaningfully be expanded and applied.

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.

Existing urban and suburban development patterns and the subsequent automobile dependence are leading to increased traffic congestion and air pollution. In response to the growing ills caused by urban sprawl, there has been an increased interest in creating more "livable" communities in which destinations are brought closer to ones home or workplace (that is, achieving travel needs through land use planning). While several reports suggest best practices for integrated land use-planning, little research has focused on examining detailed relationships between actual travel behavior and mean distance to various services. For example, how far will pedestrians travel to access different types of destinations? How to know if the "one quarter mile assumption" that is often bantered about is reliable? How far will bicyclists travel to cycle on a bicycle only facility? How far do people drive for their common retail needs? To examine these questions, this research makes use of available travel survey data for the Twin Cities region. A primary outcome of this research is to examine different types of destinations and accurately and robustly estimate distance decay models for auto and non-auto travel modes, and also to comment on its applicability for: (a) different types of travel, and (b) development of accessibility measures that incorporate this information.

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.

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.

As attention in transportation circles, increasingly focuses on encouraging pedestrian and bicycle travel, it is important that planning initiatives be informed about the safety aspects of these modes. However, recent research suggests a limited understanding of the features that affect the pedestrian and bicycle travel, particularly when it comes to safety. The report examines the information that is collected regarding pedestrian and bicycle crashes in Minnesota in comparison to other states and looks a new ways in which this data can be used increase knowledge of pedestrian and bicycle crashes. To do this exercise exploits a database of crash report information for all pedestrian and bicycle crashes in the State of Minnesota from 1998-2002. Our analysis examines general trends of pedestrian and bicycle crashes. In addition, we demonstrate that pedestrian crashes tend to occur in different locations than bicycle crashes. The final part of the paper focuses on developing a method to identify where pedestrian and bicycle crashes are more likely to occur, Poisson regression to identify the relationships between crashes and neighborhood attributes.

Transportation systems are designed to help people participate in activities distributed over space and time. Accessibility indicates the collective performance of land use and transportation systems and determines how well that complex system serves its residents. This research project comprises three main tasks. The first task reviews the literature on accessibility and its performance measures with an emphasis on measures that planners and decision makers can understand and replicate. The second task identifies the appropriate measures of accessibility, where accessibility measures are evaluated in terms of ease of understanding, accuracy and complexity, while the third task illustrates these accessibility measures. During this process a new accessibility measure named "Place Rank" is introduced as an accurate measure of accessibility. In addition, several previously-defined accessibility measures are reviewed and demonstrated in this report including Cumulative opportunity and gravity-based measures. The gravity-based measure is widely used in the literature yet cumulative opportunity tends to be easier to understand and interpret by the public, planners, and administrators. A major contribution of this research is the comparison of accessibility measures over time and among various modes. Effects of accessibility on home sales are also tested. Homebuyers pay a premium to live near jobs and away from competing workers. Accessibility promises to be a useful tool for monitoring the land use and transportation system, and assessing and valuing the benefits of proposed changes to either land use or networks.