Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility

Understanding the relative attractiveness of alternatives to driving is vitally important toward lowering driving rates and, by extension, vehicle miles traveled (VMT), traffic congestion, greenhouse gas (GHG) emissions, etc. The relative effectiveness of automobile alternatives (i.e., buses, bicycling, and walking) depends on how well streets are designed to work for these respective modes in terms of safety, comfort and cost, which can sometimes pit their relative effectiveness against each other. In this report, the level of traffic stress (LTS) criteria previously developed by two of the authors was used to determine how the streets functioned for these auto alternative modes. The quality and extent of the transit service area was measured using a total travel time metric over the LTS network. The model developed in this study was applied to two transit routes in Oakland, California, and Denver, Colorado

Low-Stress Bicycling and Network Connectivity

For a bicycling network to attract the widest possible segment of the population, its most fundamental attribute should be low-stress connectivity, that is, providing routes between people’s origins and destinations that do not require cyclists to use links that exceed their tolerance for traffic stress, and that do not involve an undue level of detour. The objective of this study is to develop measures of low-stress connectivity that can be used to evaluate and guide bicycle network planning. We propose a set of criteria by which road segments can be classified into four levels of traffic stress (LTS). LTS 1 is suitable for children; LTS 2, based on Dutch bikeway design criteria, represents the traffic stress that most adults will tolerate; LTS 3 and 4 represent greater levels of stress. As a case study, every street in San Jose, California, was classified by LTS. Maps in which only bicycle-friendly links are displayed reveal a city divided into islands within which low-stress bicycling is possible, but separated from one another by barriers that can be crossed only by using high-stress links. Two points in the network are said to be connected at a given level of traffic stress if the subnetwork of links that do not exceed the specified level of stress connects them with a path whose length does not exceed a detour criterion (25% longer than the most direct path). For the network as a whole, we demonstrate two measures of connectivity that can be applied for a given level of traffic stress. One is “percent trips connected,” defined as the fraction of trips in the regional trip table that can be made without exceeding a specified level of stress and without excessive detour. This study used the home-to-work trip table, though in principle any trip table, including all trips, could be used. The second is “percent nodes connected,” a cruder measure that does not require a regional trip table, but measures the fraction of nodes in the street network (mostly street intersections) that are connected to each other. Because traffic analysis zones (TAZs) are too coarse a geographic unit for evaluating connectivity by bicycle, we also demonstrate a method of disaggregating the trip table from the TAZ level to census blocks. For any given TAZ, origins in the home-to-work trip table are allocated in proportion to population, while destinations are allocated based on land-use data. In the base case, the fraction of work trips up to six miles long that are connected at LTS 2 is 4.7%, providing a plausible explanation for the city’s low bicycling share. We show that this figure would almost triple if a proposed slate of improvements, totaling 32 miles in length but with strategically placed segments that provide low-stress connectivity across barriers, were implemented

Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility [Summary]

DTRT12-G-UTC21This research examines transit service and the combined effect of access and mobility on the use of sustainable (transit) and active transportation (bicycling and walking). A travel time metric with transit operational data is used to show how these modes interact under a low The relative effectiveness of automobile alternatives (i.e., buses, bicycling, and walking) depends on how well streets are designed to work for these respective modes in terms of safety, comfort and cost, sometimes pitting their relative effectiveness against each other. stress network classification scheme

Optimization of Spacing of Transit Stops on a Realistic Street Network

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\u27s service area in proportion to the stops\u27 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\u27s 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

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