Click for next page ( 3

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 2
2 subsumed into public agency operating budgets and be difficult to identify as discrete project-specific costs. In this report, bicycle facilities are divided into three categories: on-street, off-street, and equipment. A bicycle facility project may include elements in one or more categories. There are different facility types within each of the categories, each of which are grouped in the cost model as described below. On-Street Facilities: On-street bicycle facilities include bike lanes, wide curb lanes, shared streets, and signed routes. Off-Street Facilities: Off-street bicycle facilities are separate from the motor-vehicle oriented roadway and are often shared use paths or trails. The trails may be adjacent to the roadway, on an abandoned railroad right of way (ROW), or on another sepa- rate facility such as through public parks. The three types of path surfaces reviewed were stone dust (fine crushed stone), bituminous concrete, and portland cement concrete. Other elements that can cause costs to vary widely are bridges, drainage, and fencing. Equipment: Bicycle facility equipment includes signs, traffic signals, barriers, park- ing, and conveyance. Installation costs will vary depending on the type of equipment. To identify and develop input data for the bicycle facility cost model, the research team reviewed a broad range of data sources. The objective was to identify unit costs for the project elements described. Data sources included transportation professionals, a literature review, and industry information drawn from completed projects, agency estimates, and bid prices. The research team used this information to develop an interactive spreadsheet for trans- portation planners that estimates costs for new bicycle facilities. The tool uses a database of unit cost to allow planners to develop a preliminary cost estimate for various facilities. The cost model provides a comprehensive estimate of capital costs including construction, procurement and installation of equipment, design, and project administration costs. Costs are based on typical standard facilities constructed in the continental United States and are represented in year 2002 dollars. Indices are provided to adjust for inflation to the project build year and regional variations in construction costs. As projects advance from early planning into design, project specifications will become more precise and the design engi- neer's estimates will provide a more reliable estimate of construction costs. Accordingly, this application includes substantial contingencies to account for both the preliminary nature of the cost estimates and the absence of detailed project specifications. MEASURING AND FORECASTING THE DEMAND FOR BICYCLING Estimating the demand for different types of cycling facilities forms the basis to esti- mate user travel time and cost savings as well as reduced traffic congestion, energy con- sumption, and air pollution. Several relatively comprehensive reviews exist that estimate the demand for non-motorized travel. Rather than simply review these existing reports, the focus here is on supplementing the knowledge gained from these reports with new per- spective and original research. Doing so provides two contributions: (1) a better under- standing of the actual amount of cycling based on different types of settings and (2) a detailed analysis to predict the amount of cycling relative to cycling facilities for the cities of Minneapolis and St. Paul, Minnesota. The former is a basis for a simple sketch plan- ning model for bicycle planners to estimate demand in local areas. The latter describes many of the difficulties associated with suggested practices of predicting demand. Such difficulties limit the applicability of traditional demand modeling applications. The findings in this report are based on the research detailing the relationship between an individual's likelihood to bike and the proximity of that individual's residence to a bike facility. The report is also based on research that indicates that the majority of bicycle riding is done by a small percentage of the population. Bicycle commuters primarily