Level Of Traffic Stress Research Articles

Low-stress bicycling connectivity: Assessment of the network build-out in Edmonton, Canada

Studies have shown that a network of safe, connected, and direct facilities can increase urban cycling levels. During summer 2017, the city of Edmonton, Canada, constructed nearly 20 km of protected bicycle lanes on its core neighborhood streets. A rapid and coordinated network implementation was preferred to the more traditional incremental approach to bike lane construction. In this paper, we evaluate the low-stress connectivity improvements afforded by this network build-out. We first classify streets and trails according to the Level of Traffic Stress (LTS) framework, which we adapt to the metric system. Using only LTS 2 and LTS 1 network links, posited to be adequate for most adults, we apply three analyses. First, we draw “bikeshed” maps, which show areas of connectivity around seven central destinations. Our comparison before and after the build-out points to a better integration of the network, with previously separate bikesheds overlapping and allowing uninterrupted low-stress travel to more destinations. This analysis also allows us to identify several central neighborhoods which are disconnected due to remaining high-stress links. Second, we generate roughly three-hundred hypothetical origins located in the central neighborhoods of the city. Reflecting the improved bikeshed integration, we observe a four-fold increase in connected origin-destination pairs. Finally, we find small reductions in trip lengths between connected pairs for some of the trips that were possible before the build-out. However, important detours are still necessary to remain on an exclusively low-stress network when compared to the shortest path using the full network, regardless of LTS. The primary contribution of our research is to develop a method of analysis, based on straightforward tools, to study the city-wide impacts of targeted infrastructure improvements and is most relevant for cities in the initial stages of bicycle network development.

Evaluating OpenStreetMap’s Performance Potential for Level of Traffic Stress Analysis

Increasingly, metropolitan areas are prioritizing growth in the share of trips taken by bicycle to improve health outcomes, transportation affordability, and environmental performance of the transport system. Evidence is building that network quality is an important determinant in bicycle commuting and route choice. One prominent metric of facility attractiveness is bicycle level of traffic stress (LTS). In tandem, OpenStreetMap (OSM) is becoming an important source of network data for routing and for generating measures of multimodal accessibility. Although there are studies that examine the completeness of OSM tags and utilize OSM data to compute LTS on networks, none of them examine the accuracy of these analyses. The goal of this paper is to evaluate the accuracy of OSM-derived LTS predictions and offer quality assurance strategies to reduce inaccurate predictions. This study compares OSM-derived LTS predictions with ground-truthed LTS scores created by Montgomery County. It finds that OSM-derived LTS networks provide comparable results to the ground-truthed data. The OSM-derived LTS scores correctly identified 89.9% of the length of the network as either high (LTS 3 or 4) or low stress (LTS 1 or 2). However, this study demonstrates there is a higher potential for error within certain street typologies and urban contexts, and that low-stress accessibility calculations can be very sensitive to even a small number of incorrectly classified segments. Finally, practices to improve the quality of OSM-derived LTS predictions and low-stress accessibility calculations are suggested.

The Keys to Connectivity: The District of Columbia’s Innovative Approach to Unlocking Low-Stress Bicycle Networks

Washington, D.C. (the District) has led the way in the adoption of state-of-the-art bicycle facilities and the innovative application of bicycle level of traffic stress (LTS) mapping for the District. Although the District’s LTS network map provides insight into bicycle network accessibility at the street level, the District needs a data-driven mechanism for identifying and prioritizing future investments to improve cycling at the system level. As part of its ongoing District Mobility Project, District Department of Transportation (DDOT) applied a geographic information system (GIS)-based network analysis to its bicycle LTS map to quantify network connectivity on low-stress District streets. This analysis was used to develop a bicycle network accessibility rating for District neighborhoods. The results revealed that though DDOT has invested in a range of innovative projects to improve accessibility in the District, only 27% of the overall network has been connected. By taking the analysis a step further and identifying the bottom 10% of accessible census blocks, DDOT both confirmed existing perceptions about the presence of disconnected low-stress “islands” in the District, and identified key gaps in the overall bicycle network. Using this information, DDOT is able to prioritize future bicycle infrastructure investments, while possessing a data-driven mechanism for assessing and communicating the accessibility benefits offered by individual investments. In addition, the methodology for developing the network accessibility rating provides a proof-of-concept for other jurisdictions looking to maximize the utility of their own LTS network maps.

An adaption of the level of traffic stress based on evidence from the literature and widely available data

Bicyclist quality of service measures are often difficult to apply on the network rather than facility level. Analyzing bicycle infrastructure on the network level is a critical process for managing bicycle infrastructure planning, design, and construction. The Level of Traffic Stress (LTS) measure fills this need for a network level measure. However, the originally proposed LTS measure leaves some gaps related to the designation of facilities and requires data that may be difficult to collect on a network level. The adapted LTS measure proposed here is based on traffic, roadway, and bikeway characteristics data available to most planning and engineering agencies and on evidence from the literature. The adapted LTS was used to classify and analyze bike network connectivity in two case studies to assess the methodology and demonstrate practical applications in infrastructure management. The first was a six-mile buffer zone of the Atlanta BeltLine Eastside Trail, and the second was a three-mile transit access zone around three transit stations in southwest Atlanta. The analysis was done in ArcGIS and provides results that can be easily interpreted by the public and decision makers, while relying on quantifiable traffic and roadway characteristics.

Fear Factor: Level of Traffic Stress and GPS Assessed Cycling Routes

Background: Cycling currently comprises only 1% of transport trips in the U.S. despite benefits for air pollution, traffic congestion, and improved public health. Methods: Building upon the Level of Traffic Stress (LTS) methodology, we assessed GPS trip data from utilitarian cyclists to understand route preferences and the level of low stress cycling connection between origins and destinations. GPS data were obtained from adult transport cyclists over multiple days. All bikeable road segments in the network were assigned an LTS score. The shortest paths between each origin and destination along bikeable roadways and along low stress (LTS 1 or 2) routes were calculated. Route trajectories were mapped to the LTS network, and the LTS and distances of observed, the shortest and low stress routes were compared. LTS maps and animations were developed to highlight where low stress connections were lacking. Results: There were 1038 unique cycling trips from 87 participants included in the analysis. An exclusively low stress route did not exist for 51% of trips. Low stress routes that were possible were, on average, 74% longer than the shortest possible path and 56% longer than the observed route. Observed routes were longer and lower stress than the shortest possible route. Conclusions: Results indicate that transport cyclists traveled beyond low stress residential areas and that low stress routes with acceptable detour distances were lacking. Cyclists appeared to weigh both route distance and quality and were willing to trade maximum directness for lower stress. GPS data provide additional information to support planning decisions to increase the impact of infrastructure investments on cycling mode share.

A behavioral modeling approach to bicycle level of service

Bicycle level of service (LOS) measures are essential tools for transportation agencies to monitor and prioritize improvements to infrastructure for cyclists. While it is apparent that different types of cyclists have varying preferences for the facilities on which they ride, in current research and practice, measures are used that are either insufficiently quantitative and empirical or lack cyclist segmentation. In this study, we conducted a detailed survey on cyclist habits, preferences, and user experience, capturing responses to videos of a bicycle traveling on road segments in the San Francisco Bay Area. The survey provided rich behavioral data, which invited both quantitative and qualitative exploration. We compared facility preferences from the survey to scores from two common measures, NCHRP bicycle level of service (NCHRP BLOS), and level of traffic stress (LTS); and we examined the responses to open-ended questions to gain insights about heterogeneity of preferences among cyclists. Finally, we applied behavioral analysis tools as a proof of concept for a new bicycle level of service measure that accounts for the segmentation of cyclist types via a latent class choice model. Combining statistics and behavioral analysis, we can improve the quality of bicycle level of service measures to make decisions driven by empirically measured cyclist preferences.

How bicycle level of traffic stress correlate with reported cyclist accidents injury severities: A geospatial and mixed logit analysis

Transportation agencies need efficient methods to determine how to reduce bicycle accidents while promoting cycling activities and prioritizing safety improvement investments. Many studies have used standalone methods, such as level of traffic stress (LTS) and bicycle level of service (BLOS), to better understand bicycle mode share and network connectivity for a region. However, in most cases, other studies rely on crash severity models to explain what variables contribute to the severity of bicycle related crashes. This research uniquely correlates bicycle LTS with reported bicycle crash locations for four cities in New Hampshire through geospatial mapping. LTS measurements and crash locations are compared visually using a GIS framework. Next, a bicycle injury severity model, that incorporates LTS measurements, is created through a mixed logit modeling framework. Results of the visual analysis show some geospatial correlation between higher LTS roads and “Injury” type bicycle crashes. It was determined, statistically, that LTS has an effect on the severity level of bicycle crashes and high LTS can have varying effects on severity outcome. However, it is recommended that further analyses be conducted to better understand the statistical significance and effect of LTS on injury severity. As such, this research will validate the use of LTS as a proxy for safety risk regardless of the recorded bicycle crash history. This research will help identify the clustering patterns of bicycle crashes on high-risk corridors and, therefore, assist with bicycle route planning and policy making. This paper also suggests low-cost countermeasures or treatments that can be implemented to address high-risk areas. Specifically, with the goal of providing safer routes for cyclists, such countermeasures or treatments have the potential to substantially reduce the number of fatalities and severe injuries.

Impacts of Level of Traffic Stress on Bikeshare Ridership in the Case of Capital Bikeshare in Montgomery County, Maryland

This research explored the relationship between level of traffic stress (LTS) and bikeshare ridership within the Capital Bikeshare (CB) network in Montgomery County, Maryland. Linear regression was used to build a model that could predict the relationship between bikeshare ridership and low-stress bicycle connections between stations, enhanced by various demographic and built environment variables that helped to define the regional context. Linear regression models were used to estimate total trips per year and to inform further studies that sought to increase ridership through improved bicycle facilities and environments. On the basis of the varying land uses within Montgomery County and the geography of CB stations in the county, two separate data sets were created to measure the ridership propensity within the northern part of the county around Rockville and Shady Grove and the southern part of the county around Bethesda and Silver Spring. The models illustrated the utility of LTS measures in identifying the effect of low-stress bicycle connections on bikeshare ridership. The models indicated that bikeshare station pairs that were connected by links with a higher percentage of low-stress facilities were correlated with higher bikeshare ridership. Station pairs that required a longer detour to achieve a low-stress route relative to a shortest-path route were correlated with lower bikeshare ridership. Application of the linear regression tool to estimate ridership on the basis of LTS and other context variables is transferable to other similar networks to draw conclusions about the relationship between quality bicycle connections and the level of bikeshare ridership.

Low-Stress Bicycle Network Mapping: The District of Columbia’s Innovative Approach to Applying Level of Traffic Stress

Washington, D.C., has been a national leader in the adoption of innovative bicycle facilities. However, with much of the low-hanging fruit already plucked (i.e., bicycle facilities already in place), the District Department of Transportation (DOT) needed a mechanism to prioritize investments. Thus the District DOT developed a bicycle level of traffic stress (LTS) map as part of a Multimodal Congestion Management Study. Existing roadway information, combined with an innovative geographic information system approach, was used to create the map and to prioritize and expedite the collection of supplemental roadway information. The results confirmed existing perceptions about the availability of bicycle facilities in the District and identified previously unidentified gaps in the overall bicycle network. In addition, the methodology used to develop the LTS network map provided a proof-of-concept for other jurisdictions to use as they look to develop their own LTS network maps. With this information, the District DOT can now prioritize future bicycle infrastructure investments. It also has a mechanism to update the LTS map as additional data are collected and new facilities are constructed.

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.

Fear Factor: Level of Traffic Stress and GPS Assessed Bicycle Routes in San Diego County

Fear Factor: Level of Traffic Stress and GPS Assessed Bicycle Routes in San Diego County

Traffic stress and bicycling to elementary and junior high school: Evidence from Davis, California

Abstract The purpose of this study is to examine the influence of stress stemming from proximity to automobile traffic on bicycling to elementary and junior high school. A growing body of evidence shows that students who walk or bike to school have higher levels of overall physical activity. Turning around the decline in active travel to school in the U.S. could thus produce improvements in the health and wellbeing of students. Despite considerable research on the influence of the urban environment on walking to school, a better understanding of the factors specific to bicycling to school is needed. We conducted a study of bicycling to school in Davis, California using data from repeated observations of bike rack counts at 11 neighborhood schools. We adopt the bicycle level of traffic stress (BLTS) methodology for estimating traffic stress from roadway characteristics as an inverse measure of bicycling comfort. We use Bayesian binomial multi-level regression models to analyze the influence of traffic stress and other covariates on bike rack counts. Results suggest that changing the street environment to reduce traffic stress (according to BLTS categorization) along routes to school in concert with encouraging shorter travel distances to school will increase the number of students bicycling and thereby increase student health.