01037953

Component

https://doi.org/10.3141/1968-07

http://scholar.google.com...orty&publication_year=2006

The introduction of the concept of level of service (LOS) and the process to measure it are important contributions of the Highway Capacity Manual (HCM). Recently, the usefulness and validity of the LOS categories have been reexamined. One underlying issue is how to handle uncertainty embedded in the definition of LOS, measurement of the flow condition, and matching the measured condition with the LOS categories. HCM suggests the “use of judgment” to handle uncertainty. The aspects of uncertainty involved in LOS analysis are examined, and different approaches to quantify the uncertainty, depending on its nature, are suggested. The truth of the statement “flow condition X is LOS = Z” is determined by a deterministic, probabilistic, or possibilistic framework, depending on the character of X and Z. For varying combinations of X and Z, different frames of analysis must be used. Six combinations of types of X and Z are identified. One extreme is when both X and Z are assumed to be precise; such is the current HCM practice. The other extreme is when the boundaries of Z and X are assumed to be vague. Between these two extremes, four cases are identified, and an appropriate analytical framework is suggested for each. Results indicate that the aspects of uncertainty should be understood and that the mathematical framework selected for analysis should be consistent with the types of uncertainty involved.

01037954

English

pp 53-62

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (6) ; References (16)

Level of service; Mathematical analysis; Measurement; Traffic flow; Uncertainty

Highway Capacity Manual

Flow conditions; Mathematical frameworks

Highways; Operations and Traffic Management; Planning and Forecasting; I21: Planning of Transport Infrastructure

TRIS, TRB, ATRI

Nov 29 2006 3:58PM

• Application of Genetic Neural Networks to Real-Time Intersection Accident Detection Using Acoustic Signals
• Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management
• Forecasting Shared-Use Vehicle Trips with Neural Networks and Support Vector Machines
• Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method
• Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data
• Kalman Filter-Based Tracking System for Automated Inventory of Roadway Signs
• Moments Analysis for Improving Decision Reliability Based on Travel Time
• Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
• Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
• Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
• Real-Time Detection of Crash-Prone Conditions at Freeway High-Crash Locations
• Travel Time Estimation with Correlation Analysis of Single-Loop Detector Data
• Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data