Level Of Traffic Stress Research Articles

Comparing distance-based and stress-based centralities to rank priority locations for cycling infrastructure investments in small-sized cities

The lack of technical guidelines to define investment priority locations is one of the barriers to cycling in emerging countries, limiting the preparation of urban mobility plans even when legally required. The objective of this paper is to propose and compare two approaches, with and without considering the cyclists’ perception of stress (assessed with the LTS, or Level of Traffic Stress), to determine the relative importance of road segments in the network and to rank priority locations for investments in cycling infrastructure. A case study was conducted in the city of Bariri (Brazil), for which the overall contribution of each network link to the identified cycling routes was mapped and ranked according to both criteria. The spatial distribution of differences between homologous ranks (i.e., ranks of the same network link according to different criteria) was also mapped, and the spatial autocorrelation between these differences was assessed by the Local Moran’s Index, allowing the identification of road segments of greater similarity and dissimilarity between the proposed approaches for resource allocation.

A Street-Specific Analysis of Level of Traffic Stress Trends in Strava Bicycle Ridership and its Implications for Low-Stress Bicycling Routes in Toronto

This study uses Strava bicycling data to investigate network level patterns of bicycle ridership in Toronto, Canada based on Level of Traffic Stress (LTS). We found that most bicycling occurred on a small fraction of the network, with just 10% of all roads and paths accounting for 75% of all bicycle kilometres travelled in 2022. Low-stress routes (LTS 1 and LTS 2) were more popular than high-stress routes for the top 80% most popular streets. The majority of bicycle kilometres travelled (84%) in LTS 2 occurred on routes with no bicycle infrastructure, highlighting the importance of quiet residential streets in forming a low-stress bike network. Despite high-stress conditions, some LTS 3 and LTS 4 streets were heavily used, suggesting infrastructure gaps in Toronto’s bicycle network.

Comparing methods and data sources for classifying bicycle level of traffic stress: How well do their outcomes agree?

Level of Traffic Stress (LTS) metrics are widely used to examine how bicyclists may perceive stress along urban streets and identify opportunities for infrastructure improvements. The intuitiveness of the original method, which condensed 18 input variables into four levels, has made LTS very popular among practitioners. Nonetheless, it can be challenging to collect all required inputs. In response, numerous alternative methods have been developed with fewer or different inputs drawn from more general sources, such as OpenStreetMap (OSM) or GIS datasets from local agencies. These methods tend to use the same four-level schema, suggesting that they are commensurate, though this may be an inappropriate assumption. We examine agreement between seven LTS methods calculated from three data sources throughout two major U.S. cities and find substantial differences between many outcomes. Interestingly, the simplest LTS method and the least precise dataset, OSM, provided the most consistent outcomes. This suggests using simpler albeit less precise approaches to improve commensurability between LTS analyses. For more detailed analyses, we recommend site studies with on-the-ground measurements rather than relying on LTS to characterize subtleties. We also encourage clear labeling of LTS methods and data sources to avoid confusion about how results can be interpreted and compared.

Evaluation of Bicycle Network Connectivity Using Graph Theory and Level of Traffic Stress

Evaluation of Bicycle Network Connectivity Using Graph Theory and Level of Traffic Stress

Bicycle Accessibility GIS Analysis for Bike Master Planning with a Consideration of Level of Traffic Stress (LTS) and Energy Consumption

Measuring the impact of bicycle infrastructure and other mobility improvements has been a challenge in the practice of transportation planning. Transportation planners are increasingly required to conduct complex analyses to provide supporting evidence for proposed plans and communicate well with both decision makers and the public. Cyclists experience two important factors on roads: (a) travel stress related to the built environment along with the traffic conditions and (b) changes in physical burden due to topography. This study develops a method that integrates an energy consumption calculation and “bicycling stress” score to take into account external conditions that influence cyclists substantially. In this method, the level of traffic stress (LTS) is used to select street segments appropriate for different comfort levels among cyclists and is combined with biking energy consumption, in addition to distance, which is used as travel impedance to consider the effects of slopes and street intersections. The integrated Geographic Information System (GIS) analysis methods are used to evaluate bicycle infrastructure improvements in the coming years in Montgomery County, MD, USA. The analysis results demonstrated that the infrastructure improvements in the county’s bike master plan are well-targeted to improve bicycling accessibility. Furthermore, the use of energy as opposed to distance to generate bikeshed areas results in smaller bikesheds compared to distance-generated bikesheds. The method presented herein allows planners to characterize and quantify the impact of bicycle infrastructure and prioritize locations for improvements.

Investigating tools for evaluating service and improvement opportunities on bicycle routes in Ohio, United States

Over the past two decades, transportation engineers and urban planners have become increasingly interested in using performance measures to capture roadway infrastructure effects on bicyclists' comfort levels. In this study, a tool is developed for evaluating Ohio's local roads and identifying opportunities for improvement. This begins by assessing the available data sources required to develop a customized model for Ohio, United States. Stakeholder interviews and data assessment outcomes indicate that most local jurisdictions do not have access to all relevant data for a complete bicycle level of traffic stress (LTS) analysis. Therefore, we extend the original bicycle LTS framework by formulating effective methods to deal with missing data. The designed methods are based on Ohio's functional classification system, and existing data on speed limits, traffic volumes, and bicycle facility widths. Our approach enables agencies to conduct bicycle LTS analysis when critical data elements are missing (such as posted speed limits, traffic volumes, and bicycle facility widths). For example, compared to the assignments with completed Mid-Ohio region data, the proposed approach reaches a 90% match in bicycle LTS score assignments in urban areas when road traffic volumes are missing. Local communities in Ohio and beyond can adopt the proposed approach to achieve interim and temporary results when collecting accurate data is not feasible due to time and cost considerations.

How much traffic stress can cyclists endure?

Bicycles are a desirable but underutilised form of travel in many cities. A main barrier to a large uptake of cycling for travel is traffic safety. To assess how much traffic stress cyclists can endure and how this stress affects route choices, we have developed an index called Average Traffic Stress (ATS). This index aggregates several measurable characteristics, but the key ones are the trip distance and a road segment’s Level of Traffic Stress (LTS). Using Brisbane, Australia, as a case study, we analyse 3,304 GPS-tracked cycling trips and apply a multilevel regression model to link ATS to the individual characteristics of cyclists, as well as environmental characteristics. We find that younger male cyclists who make longer trips at higher trip speeds experience higherATS. Recreational trips produce less ATS. Flat topography, a disconnected street network, and a lack of cycling infrastructure are associated with higher ATS. Cyclists intuitively select routes with less car traffic and/or better cycling infrastructure to achieve the lowest level of ATS (rather than selection routes with the shortest distance or the flattest topography).

The built environment, networks design, and safety features: An analysis of pedestrian commuting behavior in intermediate-sized cities

Choosing to walk instead of using private cars or public transit has many direct benefits for the individual and collective benefits for urban populations indirectly (e.g., in the reduction of air pollution). In this study, walking preferences are analyzed taking into account the built environment, network design, and safety features for 15 intermediate-sized Iranian cities. Using a stated preference survey, a sample of more than 3600 work-trip cases was created and multinomial logit modeling was employed to determine significant contributing factors to a preference to walk. The findings indicate that network design variables have the greatest impact on the changes in walking mode choices, and improving them will increase walking choices by more than double. Safety variables increase walking choices by about 12%. Built environment factors only increase walking by about 9%. The results demonstrate that variables such as the level of traffic stress, intersections traffic control, pedestrian utilities, street network connectivity, and marked crosswalks and pedestrian bridges have the most influence on choosing to walk. Also, the modal shift analysis shows that changes in the quality of the variable groups have substantial effects on the choice to walk. Investments in upgrading the built environment, network design, and safety could lead to more than 47% of trips shifting from buses and private cars to walking. The proposed choice behavior analysis framework can be applied to active travel planning and policy evaluation. This research contributes to the growing debate about how to best assess, and model different approaches to active travel development for better public policy.

Level of Pedestrian Stress in Urban Streetscapes

Several service and performance indicators (SPIs) have recently been developed to evaluate pedestrian infrastructures, such as different walkability indexes and pedestrian level of service (P-LOS). Given that stress has become a popular SPI for bicyclists (level of traffic stress [LTS]), this study addressed stress for pedestrians as an SPI by relating measurable attributes of different urban street segment environments with pedestrian perceived stress. A stress-based pedestrian SPI was proposed to classify street segments into four levels of pedestrian stress. A total of 1,043 pedestrians across 30 segments in Bogotá were surveyed about their perceived stress. Multimodal traffic counts, location audits, and layout dimensions were collected in each segment to estimate an ordered probit model that explained four stress categories. Physical infrastructure attributes and traffic operational conditions were found to affect perceived stress at given locations. The similarities between the proposed stress index with the LTS and P-LOS are discussed, and the proposed SPI is applied to pedestrian infrastructure planning and design in light of sustainable transportation goals.

Assessing the applicability of the cyclists’ Level of Traffic Stress (LTS) classification to a medium-sized city in a developing country

IntroductionFocusing on the challenge of minimizing the level of stress to which cyclists are exposed to when cycling, the objective of this study is to assess the applicability of an important index to classify routes in terms of stress for cycling, the Level of Traffic Stress (LTS), to a medium-sized city in a developing country. MethodFor this purpose, physiological measures of stress of 15 cyclists, recorded while cycling on roads with different characteristics in the city of São Carlos, Brazil, were used to create a scale that was then compared to the LTS. In addition, applying the Random Forests technique using variables identified with video recordings collected during the trips allowed us to identify relevant causes of stress, and thus incorporate them into the LTS classification to improve their suitability to represent real-life situations. ResultsThe results showed no correlation between the LTS classification and the actual stress experienced by cyclists, but also showed that the LTS classification can be improved, bringing it closer to the actual stress experienced by cyclists, by simply adding two factors, which can be easily obtained: uphill grades and the presence of roundabouts. ConclusionsWe showed that the use of the two indicators we proposed, associated with the LTS method, are particularly interesting to classify routes according to cyclists’ stress, especially in cases when some variables (e.g., traffic volume) used in other indexes are difficult to obtain. A limitation of this study is that the data refer to a city with specific characteristics. Therefore, future works should address the validity of the method in places with different social, infrastructure and climatic characteristics and in a sample with different characteristics of gender, age and aptitude to cycle.

Mining dockless bikeshare data for insights into cyclist behavior and preferences: Evidence from the Boston region

• Studied cyclists’ behavior using en route GPS records from a dockless bikeshare system. Commercial areas have excess origins, while residential areas have excess destinations. • Cyclists often use sidewalks, other footpaths , and parking lot drives. • 36% of trips involve contraflow on a one-way segment. • Due to underdeveloped bike network, only 7% of trips are 100% on low-stress links. Emerging micromobility services provide not only new transportation options, but also valuable sources of data for researchers and planners seeking to understand traveler behavior and preferences and to improve transportation networks. Dockless systems for shared bicycles and electric scooters have recently begun operating in many American cities, providing data not only on trip start and end, but also on the route followed. In this study, we analyze data from more than 110,000 dockless bikeshare trips in Boston’s suburbs. About 15% of trip ends were within 100 m of a transit station, indicating that bikeshare serves many more functions than access to transit. More than 40% ended in a town or village center, suggesting that bikeshare may support the local economy. From examining GPS traces, it was clear that – contrary to the assumptions of common bike routing algorithms – bike riders often use sidewalks, other footways and soft-surfaced paths, parking lots, and driveways, and 36% ride the wrong way on one-way streets. An examination of the streets with high contraflow volumes indicates two common profiles. One is streets whose travel channel is so narrow that either the bike or vehicle has to pull into a parking lane to let the other pass; those streets typically have low traffic speeds and very low traffic volumes. The other is streets whose travel channel is wide enough for a bike and car to pass without either yielding to the other; they typically have greater vehicular speed and volume, but still only one lane. After classifying streets by level of traffic stress, we find that only 7 percent of trips use exclusively low-stress links, and that about 40 percent of bike-miles are ridden on low-stress links. These low percentages are consistent with an underdeveloped bike network that forces riders to use through streets for all except ultra-local trips; it may also reflect a prevalence of sidewalk riding on major arterials.

An empirical reappraisal of the level of traffic stress framework for segments

The Level of Traffic Stress (LTS) framework is widely used to assess the suitability of roadway environments for cycling. Its main strength is the identification of infrastructure characteristics that meet the needs of different cyclist groups. The four levels of infrastructure ratings, LTS 1 to LTS 4, roughly map to the cyclist types defined by the Four Types of Cyclist typology. Despite its popularity, the LTS framework has several limitations, including reliance on a cyclist typology that was developed subjectively, and a lack of empirical evidence to define thresholds between levels. This work builds on our previous empirically-based findings that cyclists form three groups rather than four: Uncomfortable or Uninterested, Cautious Majority, and Very Comfortable Cyclists. We use survey data from Edmonton, Canada, to update the LTS framework such that levels match the three types of cyclists. Direct infrastructure ratings, binary logistic regression, and route choice data provide the empirical foundation to determine infrastructure characteristics that are suitable for the three types of cyclists. This adjusted framework is called Level of Cycling Comfort (LCC). We apply the framework to Edmonton and compare connectivity outcomes using both LTS and LCC frameworks. Overall, the LCC framework yields more conservative estimates of connectivity.

Micromobility Station Placement Optimization for a Rural Setting

Micromobility is an evolving form of transportation modality that uses small human- or electric-powered vehicles to move people short distances. Planners expected that bike sharing, the first form of micromobility, would reduce traffic congestion, cut travel cost, reduce pollution, enable connectivity with other modes of transport, and promote public health. However, micromobility options also brought new challenges such as the difficulty of placement decisions to encourage adoption and to minimize conflict with other transport modes. Sound deployment decisions depend on the unique environmental characteristics and demographics of a location. Most studies analyzed deployments in high-density urban areas. This research determines the best locations for 5 new bike-sharing stations in Fargo, North Dakota, a small urban area in the rural United States. The workflow combines a geographic information system (GIS), level of traffic stress (LTS) ratings, and location-allocation optimization models. The spatial analysis considered 18 candidate station locations and eliminated those that fell within the 700-meter isochrone walking distance of the 11 existing stations. This case study demonstrates a scalable workflow that planners can repeat to achieve sustainable micromobility deployments by considering the land use, population density, activity points, and characteristics of the available pathways in their unique setting.

Towards an enriched framework of service evaluation for pedestrian and bicyclist infrastructure: acknowledging the power of users’ perceptions

More and more cities worldwide are striving for sustainability and livability. Measuring the service or performance of local-scale spaces for pedestrians and bicyclists to better understand how to provide “walkable” and “bikeable” environments is key in this endeavor to enhance active transportation. These pedestrian and bicycle service or performance indicators, such as Level of Traffic Stress or Level of Service, relate measurable characteristics with a perceived proxy of the performance or service, such as comfort, satisfaction, or quality of service (QoS). The purpose of this study is to propose and validate a framework that integrates user-oriented inputs to the existing traditional supply-oriented variables to explain the QoS in segment roadways in urban environments for active modes. The conceptual framework underlying this study considers the contribution of individual perceptions, in addition to the traditionally considered operational and geometry variables, to explain the perceived QoS of pedestrian and bicyclist infrastructure. The framework is tested via two separate and independent surveys for pedestrians and bicyclists. Evidence determined the relative importance of these supply-oriented and user-oriented factors to explain the QoS. The superior explanatory power of the perception variables and in terms of the variables that explain the individuals’ perceived QoS justify the framework for both pedestrians and bicyclists.

Incorporating Low-Stress Bicycling Connectivity into Expanded Transit Service Coverage

The speed advantage in bicycling over walking is believed to ease first-and-last mile (F&LM) travel and expand transit service coverage. To quantitatively investigate the potential effect of using bicycle as a F&LM connector, the paper measures and compares the impacts of walking and bicycling F&LM access on transit service coverage. In the estimation of transit service coverage, F&LM travel decay functions representing the attractiveness of public transit that declines with increasing walking/biking time to access transit facilities and the spatial boundaries of transit catchment areas are developed using GPS trajectory data collected from the latest Cincinnati Household Travel Survey in Hamilton County, Ohio. Level of traffic stress is used to evaluate the bicycle suitability of streets and bike network connectivity. Based on the F&LM distance decay functions and low-stress bike network connectivity, the transit service coverage area as well as the transit-served population and employment in Hamilton County, Ohio, are estimated. Results show that more population can reach transit services and therefore employment by bicycling than walking. Meanwhile, disadvantaged groups, that is, low-income and zero-car population, can be better served by transit if using bicycle as the F&LM connector. In addition, low-stress bicycling connectivity is a significant factor determining the bicycle-transit service coverage, and a well-connected low-stress bike network with quality bikeways is crucial to guaranteeing that. These findings can be used as references to assist planners in their decision-making process to achieve better mobility and accessibility.

The Impact of COVID-19 Cycling Infrastructure on Low-Stress Cycling Accessibility: A Case Study in the City of Toronto

This paper investigates the impact of COVID-19 cycling infrastructure on Toronto’s low-stress cycling accessibility. We calculated the level of traffic stress (LTS) for the road network with and without the cycling infrastructure built in response to the COVID-19 pandemic. We then computed the accessibility to populations, jobs, food stores, and parks for each dissemination area at each LTS. We find that the COVID-19 cycling infrastructure increased low-stress population and job accessibility by 10.4% and 22.3% respectively, and granted food and park access to 54,023 and 3,178 people. Accessibility gains were largest in areas where new infrastructure linked with preexisting networks.

Level of traffic stress-based classification: A clustering approach for Bogotá, Colombia

The Level of Traffic Stress (LTS) is an indicator that quantifies the stress experienced by a cyclist on the segments of a road network. We propose an LTS-based classification with two components: a clustering component and an interpretative component. Our methodology is comprised of four steps: (i) compilation of a set of variables for road segments, (ii) generation of clusters of segments within a subset of the road network, (iii) classification of all segments of the road network into these clusters using a predictive model, and (iv) assignment of an LTS category to each cluster. At the core of the methodology, we couple a classifier (unsupervised clustering algorithm) with a predictive model (multinomial logistic regression) to make our approach scalable to massive data sets. Our methodology is a useful tool for policy-making, as it identifies suitable areas for interventions; and can estimate their impact on the LTS classification, according to probable changes to the input variables (e.g., traffic density). We applied our methodology on the road network of Bogotá, Colombia, a city with a history of implementing innovative policies to promote biking. To classify road segments, we combined government data with open-access repositories using geographic information systems (GIS). Comparing our LTS classification with city reports, we found that the number of bicyclists’ fatal and non-fatal collisions per kilometer is positively correlated with higher LTS. Finally, to support policy making, we developed a web-enabled dashboard to visualize and analyze the LTS classification and its underlying variables.

Commuting patterns and bicycle level of traffic stress (LTS): Insights from spatially aggregated data in Franklin County, Ohio

This study analyzes the relationships between bicycle network design and commute mode shares in Franklin County, Ohio. We first adopt the bicycle level of traffic stress (LTS) criteria to measure bicycle network design. Then, we develop a fractional multinomial logit model to simultaneously estimate the determinants of the shares of commuters who regularly go to work by carpooling, riding public transit, bicycling, and walking. The analytical results show that increasing the proportion of low-stress road segments (LTS 2) is statistically significant and positively associated with the share of bicycle commuters at the census block group level. However, we do not observe a significant relationship between the proportion of very low-stress road segments (LTS 1) and the share of bicycle commuters. This study suggests that social and cultural relevant factors are more important predictors of reductions in automobile use as compared to those factors representing road conditions.

Validation of Bicycle Level of Traffic Stress and Perceived Safety for Children

The level of traffic stress (LTS) methodology was developed to measure, track, and improve the suitability of bicycle networks. Thanks to the simplicity of its data needs and interpretation, LTS has been implemented by several states, regions, cities, non-profits, and researchers. However, relatively few validations of the methodology exist. There is a specific gap in relation to safety perceptions for children, an important group since it serves as the critical population for LTS 1. This study validates LTS using a survey of parents in Denver, Colorado, in which they are asked about perceived safety and biking allowance relative to roadway design characteristics. After the LTS score and biking allowance rates for 612 roadway scenarios are determined, a one-way analysis of variance (ANOVA) is used to determine the suitability of LTS for children. Findings suggest that while LTS 1 and LTS 4 align well with stated preferences, parents told that their children would be able to tolerate some roadway conditions—when allowing for adult supervision—that are currently considered LTS 2 or even LTS 3. These scenarios are primarily on low-volume roadways that have bike lanes. By further refining LTS, it is hoped to ensure that all populations have access to safe and comfortable bicycle facilities.

Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data

Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the applicability of BLOS rating schemes for the estimation of whole-journey route choice. The methods evaluated share a common trait of being applicable for mixed traffic urban environments: Bicycle Compatibility Index (BCI), Bicycle Stress Level (BSL), Sixth Edition Highway Capacity Manual (HCM6), and Level of Traffic Stress (LTS). Routes are generated based on BLOS-weighted networks and the suitability of these routes is determined by finding the percentage overlap with empirical route choices. The results show that BCI provides the best match with empirical route data in all five origin–destination pairs, followed by HCM6. BSL and LTS which are not empirically founded have a lower match rate, although the differences between the four methods are relatively small. By iterating the detour rate that cyclists are assumed to be willing to make, it is found that the best match with modelled BLOS routes is achieved between 15 and 21% additional length. This falls within the range suggested by existing empirical research on willingness to deviate from the shortest path, however, it is uncertain whether the method will deliver the comparable findings in other cycling environments.