Near-Side, Far-Side, Uphill, Downhill: Impact of Bus Stop Location on Bus Delay

One of the factors affecting where bus stops should be located is the expected delay associated with the stop location. On hills, the effect of gravity on already weak diesel engines can lead to considerable additional delay if a bus has to accelerate from a stop. An empirical bus acceleration profile, modified to account for gravity, was applied to constant grade, sag curve, and crest curve profiles. The marginal impact of grade on stopping delay ranged from –4 to 11 s, depending on grade. At signalized intersections, a deterministic model was created that accounted for deceleration, acceleration, and queue interference. Relative to a stop placed away from an intersection, far-side stop placement either causes a very small reduction in delay or has no effect. Near-side placement can reduce delay in a few cases such as reserved bus lanes, but more often it increases delay, sometimes considerably depending on factors such as red ratio, volume:capacity ratio, cycle length, and stop setback. Measures that reduce interference with the queue tend to reduce the net delay from a near-side location; these measures include increasing stop setback, shortening cycle length, and giving the bus a (near) exclusive lane. Results are presented with default adjustments for hills and signalized intersections that can be used in the context of a stop spacing study

Stop Spacing Analysis Using Geographic Information System Tools with Parcel and Street Network Data

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

Parcel-Level Modeling to Analyze Transit Stop Location Changes

Because of how important walk access is for transit travel, service changes that affect walking distance, such as route or stop relocation, call for modeling at a fine enough level to accurately reflect the often arbitrary aspects of the access network and of demand distribution within a zone. Case studies of stop relocation in Boston and Albany demonstrate the feasibility of parcel-level modeling on the unabridged street network using an assessor’s database. Parcel-level demand is estimated by allocating observed on/off counts as a function of a parcel’s land-use type, size (e.g., gross floor area), and location factors. With actual land-use and street network data, we show how stop service areas can deviate substantially from the simple geometric shapes that follow from assuming airline or rectilinear travel, and demand distribution can be far from uniform within a zone. These factors can significantly favor particular transit stop locations

Parcel-Level Modeling to Analyze Transit Stop Location Changes

Because of how important walk access is for transit travel, service changes that affect walking distance, such as route or stop relocation, call for modeling at a fine enough level to accurately reflect the often arbitrary aspects of the access network and of demand distribution within a zone. Case studies of stop relocation in Boston and Albany demonstrate the feasibility of parcel-level modeling on the unabridged street network using an assessor’s database. Parcel-level demand is estimated by allocating observed on/off counts as a function of a parcel’s land-use type, size (e.g., gross floor area), and location factors. With actual land-use and street network data, we show how stop service areas can deviate substantially from the simple geometric shapes that follow from assuming airline or rectilinear travel, and demand distribution can be far from uniform within a zone. These factors can significantly favor particular transit stop locations