Low-Stress Bicycle Networks

pedbikeimages.org - Russ Roca

Riding a bicycle on a greenway feels completely different than riding a bicycle on a busy street surrounded by cars and trucks. By recognizing that bicyclists experience varying levels of comfort depending on facility type and individual skill level, planners can identify how well a bicycle network accommodates certain users. Level of Traffic Stress (LTS) is an alternative to the traditional Level of Service (LOS) measurement, which categorizes facilities primarily based on capacity and traffic flow. Even though LOS has been adapted to evaluate multimodal networks, this approach has major drawbacks—LOS treats all users the same and the resulting classification of letter grades "A" through "F" do not convey significant meaning. The Mineta Transportation Institute developed the LTS classification for bicycle network segments, which considers the stress associated with specific routes and identifies networks that bicyclists of a certain ability will feel comfortable using.

pedbikeimages.org - Adam Coppola Photography

LTS values range from 1–4, which relate to the four types of bicyclists identified by Roger Geller and refined by additional research. LTS 1 represents an acceptable level of stress for most children, LTS 2 is considered comfortable by the majority of adults including people who are “interested but concerned”, LTS 3 accommodates experienced bicyclists that are “enthused and confident”, and LTS 4 is stressful for everyone except the “strong and fearless”. Segments are evaluated across multiple characteristics and the overall rating is determined by the most stressful value. For example, if a segment is entirely LTS 2 except for one particularly dangerous intersection that is LTS 4, the segment is considered LTS 4.

Mapping bicycle systems by LTS reveals islands of isolated networks. This makes it easier to identify where low-stress links are needed to connect islands of low-stress facilities. LTS 2 facilities, which are based in Dutch design, should be prioritized as they accommodate the vast majority of bicyclists. While Level of Traffic Stress has gained recognition, some cities have adapted the terminology from low-stress to high-comfort. Looking forward, LTS classification will likely incorporate crowd sourced data.


Pedestrian and Bicycle Information Center
Several PBIC resources provide guidance on bicycle network improvements and how to communicate different types of bicycle infrastructure to the public.

Federal Highway Administration
In addition to detailing engineering considerations for low-stress networks in urban and rural areas, the FHWA also explains how to assess the performance of such networks.

The following resources from this PeopleForBikes program focus on building and connecting low-stress bicycle infrastructure.

Association of Pedestrian and Bicycle Professionals
Design for Cyclist and Pedestrian Comfort: This webinar compares the Mineta Transportation Institute's LTS evaluation process to a method created by San Francisco's Department of Public Health. Free for APBP members.


Low-Stress Bicycling and Network Connectivity (2012): As the primary resource on LTS, the Mineta Transportation Institute explains LTS methods and evaluation criteria.

Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility (2017): In this study, the Mineta Transportation Institute builds on its initial report by analyzing the relationship between transit, pedestrian, and bicycle networks in terms of LTS.

Utilizing Ego-centric Video to Conduct Naturalistic Bicycling Studies (2016):This paper by the National Institute for Transportation and Communities explores the effectiveness of using on-board videos and sensors to better understand bicyclists' comfort levels.

Evaluating the Use of Crowdsourcing as a Data Collection Method for Bicycle Performance Measures and Identification of Facility Improvement Needs (2015): Portland State University researchers find proof in the theory behind LTS by studying bicyclist route choice and comfort level data collected by smartphones.