SUMMARY

Traffic Capacity Level of Service: Adaptations and Usage

The level of service (LOS) concept has been used for over 50 years by state departments of transportation (DOTs) for policy setting, planning, analysis, and communication efforts. Individual adoption of the LOS framework by DOTs has generally been consistent with the Highway Capacity Manual (HCM) although usage and application may vary across different state DOTs. Some state DOTs may also have tailored their respective LOS definitions and applications for local conditions, multimodal analysis, and threshold values such as adjustment of the HCM’s LOS category definitions. Additionally, there are differences in how DOTs use big data and modern communication tools in their LOS analyses. As a result of this widespread application of the HCM concepts, there is a need for a greater understanding of DOT practices with respect to the usage, adaptations, and adoption of the HCM LOS framework. The objective of this synthesis is to document state DOT practices concerning the usage and adaptations of the HCM LOS framework for varying levels of applications, such as policy setting, planning, operational analysis, and communication practices. The findings of this synthesis report may assist state DOTs in improving their LOS practices and policies. This report may also assist practitioners and researchers in identifying gaps and needs for future research related to the HCM LOS framework.

This synthesis was based on a literature review of the existing DOT practices for using LOS or other performance measures for various applications. Next, a survey was distributed to all 50 state DOTs and the District of Columbia DOT. Forty-one of the 50 states and the District of Columbia DOT responded to the survey, which corresponds to a response rate of 80%. Finally, follow-up interviews with six selected DOTs were conducted for the development of case examples.

The synthesis addresses:

• The use of the HCM LOS framework or other adopted frameworks for various tasks related to policy, planning, design, and operations.
• The exact LOS methodologies adopted by the DOTs for each transportation facility and mode.
• Deviations and adaptations of the HCM LOS methodologies.
• Use of traffic analysis tools for LOS analysis and communication purposes.
• Data availability for LOS determination, and the role of big data for determining LOS criteria.

The literature review revealed that state DOTs use the HCM LOS framework for analyzing transportation projects at various stages, including planning, preliminary engineering, design, and operations. With a few exceptions, state DOTs use the LOS as a target for design purposes of various facilities. Most DOTs select LOS D for urban/high-volume areas and LOS C for rural areas. Some common implementations of the LOS framework include

planning and design of facilities, intersection control evaluation, interchange access change requests, and traffic impact studies. For operational analysis, agencies typically report LOS and other measures of effectiveness (MOEs) that capture different aspects of traffic operations and oversaturation. For multimodal analysis, some state DOTs use the level of traffic stress (LTS) as an alternative to the HCM multimodal LOS.

The survey responses and consequent case examples revealed that LOS has a wide range of applications and is primarily used by state DOTs for policy, planning, and operational applications, such as traffic impact studies, intersection control evaluation, roadway design, interchange access requests (IARs), transportation improvement program projects, as well as a tool to communicate impact to the public. It is less commonly applied to multimodal projects, transportation system performance monitoring, and sustainability-related policies. For those applications in which LOS is not regularly or typically considered, DOTs use various other performance measures, such as volume-to-capacity (v/c) ratio, vehicle miles traveled (VMT), vehicle hours traveled (VHT), travel time, travel time reliability, and air quality metrics.

The survey solicited information on the use of LOS by facility type and travel mode. A total of 15–20 DOTs indicated they do not use specific manuals for multimodal evaluations (depending on the facility type). Seven state DOTs indicated they do not use the HCM method for multimodal LOS. Alternative methodologies include LTS, various indices from NCHRP Research Report 948 (Kittelson & Associates, Inc. et al., 2021), simplifications to the HCM multimodal LOS method, and design-related manuals such as the FHWA Bikeway Selection Guide (FHWA, 2019). Nine DOTs reported that complexity, inaccuracy, and inability to capture user perception are major reasons for not using the HCM multimodal LOS framework. The Wyoming DOT argued that safety-related performance measures are more appropriate for multimodal LOS. Washington State DOT suggested that LTS can help bypass the limitations in the HCM of not accurately capturing demand.

Respondents also indicated the SIDRA method as an alternative to the HCM roundabouts method (Akçelik and Besley, 2022). Other reported limitations regarding the auto mode include the manipulability of LOS, lack of subcategories for LOS F, and inaccuracies in specific methods like freeway weaving and passenger car equivalent (PCE) values on steep roadways.

The survey and interviews also indicated that state DOTs have made modifications to the HCM methods to account for their local conditions. Modifications related to the auto mode include state-specific calibration factors, different LOS thresholds and capacity values for certain facility types (e.g., freeway segments, urban streets, roundabouts, ramp terminals, and alternative intersections), different PCE values, and guidance on the use of saturation flow rate and right-turn-on-red (RTOR).

Regarding a limitation of the HCM LOS related to capturing oversaturated conditions, 20 DOTs reported using additional performance measures to describe and quantify the severity of congestion beyond LOS F. These DOTs also reported the spatial extent of congestion, the temporal duration of congestion, the amount of excess demand-to-capacity ratio, the unmet demand and the change in travel time and speed. Minnesota DOT has developed five sub-levels of LOS F based on density at freeway segments. More details on Minnesota DOT methodology are provided in Chapter 3 and Chapter 4.

State DOTs use multiple tools when conducting an LOS study. Depending on the facility type analyzed, between 30 and 37 of the 41 respondents reported using analytical tools to analyze all facilities covered in the HCM, except pedestrians and bicycles. Many DOTs (between 20 and 27, depending on the facility type) use microsimulation primarily for

freeways and multilane highways, ramp terminals, urban streets, roundabouts, and signalized intersections. DOTs that perform LOS analysis for pedestrians and bicycles reported using mostly sketch-planning or analytical tools. Microsimulation and analytical tools are also widely used for visualization purposes and communicating LOS to stakeholders and the public. Some DOTs also use probe data for visualization purposes.

Emerging technologies and big data are used to calibrate LOS methodologies, supplement data requirements, and provide real-time congestion monitoring. Some DOTs use big data to skip analysis steps and directly collect performance measures, while others do not see an impact of emerging technologies and big data on LOS criteria or threshold values.

Overall, future work by the research community could lead to consistent application of the LOS framework among the state DOTs. Future research could investigate the following:

• Development of LOS F subcategories that could assist state DOTs in quantifying congestion and aligning the LOS framework with continuous performance measures representing congestion, such as v/c ratio, queue length, etc.
• Peer-learning and exchange of knowledge between the DOTs on simplifying and expediting their traffic analysis efforts, applying the newer HCM methods, improving their analysis processes, and better managing quality control.
• Better aligning the LOS thresholds obtained from big data with the HCM definitions to ensure that these data can be used accurately for traffic analysis and LOS determination.
• Revisiting the HCM LOS thresholds, either working to standardize them across similar methodologies or providing a rationale for the existing differences. Deficiencies and inconsistencies in the HCM methods and LOS criteria were highlighted through the survey and case examples.