Hourly Traffic Flow Estimation Using Speed and AADT Data
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Solution Overview
Problem
Current methods using annual average daily traffic (AADT) and traffic speed data are insufficient for accurate performance evaluation and active management of transportation networks, as they fail to provide comprehensive traffic flow profiles and hourly traffic flow estimates, which are essential for effective road network planning and operation.
Innovation Solution
A system and method that reconstructs traffic flow profiles by combining AADT data with traffic speed data, computing daily directional flows, and estimating hourly flow distribution profiles, identifying peak directions and times, and categorizing traffic patterns into morning, afternoon, Saturday, Sunday, and double peak periods, to generate accurate hourly traffic flow profiles for roadway segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only traffic speed data is used, then data collection is simple, but traffic flow information is insufficient
Solution Approach 1:
The patent combines AADT data (providing traffic volume information) with traffic speed data (providing speed information) to create a comprehensive traffic flow estimation system. This merging of different data sources resolves the contradiction by maintaining data collection simplicity while recovering the lost traffic flow information through computational integration of multiple data types.
Solution Approach 2:
The patent introduces computational models and algorithms as intermediaries that process and integrate AADT data with traffic speed data. These intermediary processing mechanisms transform the limited speed data into meaningful traffic flow estimates by applying mathematical relationships and traffic engineering principles, thereby recovering information without requiring direct flow measurements.
2Device complexity
If AADT data is used alone, then traffic volume measurement is simple, but hourly traffic flow estimation is insufficient
Solution Approach 1:
The patent applies dynamic adjustment by using traffic speed data to modulate the static AADT values according to actual traffic conditions at different times. The system dynamically scales AADT-based estimates using speed ratios and temporal patterns to produce accurate hourly flow estimates, transforming a static measurement into a dynamic estimation system that adapts to varying traffic conditions.
Solution Approach 2:
The patent changes the parameter representation from annual averages to hourly estimates by introducing time-dependent scaling factors derived from traffic speed data. The system transforms the single parameter AADT into multiple hourly flow estimates by applying temporal distribution patterns and speed-based adjustments, thereby improving measurement precision without increasing field measurement complexity.
3Loss of information
If directional flow analysis is added to AADT data, then traffic pattern understanding improves, but data processing complexity increases
Solution Approach 1:
The patent segments the bidirectional AADT data into directional components by analyzing traffic speed patterns in different directions. The system divides the total traffic flow into direction-specific flows (e.g., northbound vs. southbound) by identifying peak direction patterns and applying directional split factors, thereby recovering directional information through computational segmentation rather than physical separation of measurement systems.
Data Source
AI summary
A framework for performance evaluation and active management of a transportation network infrastructure reconstructs traffic flow profiles by modeling annual average daily traffic data and collected traffic speed data to estimate an hourly traffic flow profile for a roadway segment, or link. Total daily flow for a link is derived from the corresponding annual average daily traffic data for that link, and is adjusting by the day of week and the monthly seasonal factors. An hourly flow distribution profile for a roadway link is then constructed using the traffic speed data relative to that link.