When streets with high traffic stress—on which the mainstream population is unwilling to ride a bike—are removed, the remaining network of streets and paths can be fragmented and poorly connected. This paper describes the development of methods to visualize and to analyze the lack of connectivity in a low-stress bicycling network. A proposed measure to evaluate bicycling networks is the fraction of origin–destination pairs, which are connected without the use of high stress, without excessive detour, and with the origin–destination pairs weighted by travel demand. A new method is proposed to classify segments and crossings into four levels of traffic stress (LTS) on the basis of Roger Geller’s classification of the cyclist population and Dutch design standards, which are known to attract the mainstream population. As a case study, every street in San Jose, California, was classified by LTS value. Maps that showed only lower stress links revealed a city divided into islands within which low-stress bicycling was possible, but these islands were separated from one another by barriers that could be crossed only with the use of high-stress links. The fraction was 4.7% of home-to-work trips up to 6 mi long that were connected at a low LTS value. The figure would almost triple if a modest slate of improvements were implemented to connect low-stress streets and paths with each other.

Geographic databases and computing tools present an opportunity for improved analysis of bus stop location or spacing changes. Changes in stop location affect walking, riding, and operating cost; of these, the impact on walking is the most important and complex. Traditional models and design rules for stop spacing do not model the impact on walking precisely, because they assume uniform demand density and unobstructed walking paths. This paper discusses an analysis procedure based on a parcel-level geographic database (supplied by a local government body such as the city tax assessor) and a street network. Walking paths and stop service area boundaries are based on shortest path and Voronoi diagram methods applied to the street network. Data on each parcel's land use and development intensity are used to distribute historic on–off counts and thus estimate the demand arising in each parcel. For alternative stop sets, then, the demand at each stop, walking distance, riding time, and operating cost impacts can be determined. Case studies on transit routes in Boston, Massachusetts, and Albany, New York, demonstrate the method's practicality. Results confirm the benefits of a recent stop rationalization effort in Boston and show how proposed stop elimination and relocation plans can be adjusted to yield a greater net benefit to society.

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A discrete model of bus stop location in which candidate stops are either selected or not has several practical advantages over classical continuum models. An evaluation method for stop sets that uses parcels as units of demand and the street network to model walking paths between transit stops and parcels has been proved effective and realistic. In this framework, the on–off counts at existing stops are used to allocate demand to the parcels in each stop's service area in proportion to the stops' trip-generating ability. The result is a demand distribution that matches existing counts and reflects variations in land use. However, with demand modeled on the street network, the placement of service boundaries midway between neighboring stops becomes invalid because of irregularities in the network of access streets and curves in the transit route. The dependence of a stop on more than its immediate neighbors for determination of its service area complicates the process of optimization of stop locations by use of dynamic programming. The proposed solution expands the state space so that a stop's service area is dependent on the two prior and the two succeeding stops. The resulting dynamic programming model was tested on two bus routes and found solutions that were better than the existing stop set and the stop sets proposed by consultants by use of simple yet state-of-the-art models. This paper describes a method for optimization of stop locations on an existing route that includes realistic and localized estimates of its impacts on walking and riding times and operating cost.

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