Traffic Velocity Estimation Using Phase-Based Smoothing
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Solution Overview
Problem
Existing traffic velocity estimation methods face challenges due to sparse and incomplete data from Floating Car Data (FCD), which limits accuracy, especially in reconstructing traffic phases and velocities, particularly in congested areas where velocities can be low and variable.
Innovation Solution
The Phase-based Smoothing Method (PSM) calculates criteria probabilities for traffic phases, accounts for uncertainty, and aggregates phase-dependent velocity estimates to provide accurate traffic velocity estimates using FCD, even with sparse data, by employing convolution kernels and fuzzy decision criteria to identify traffic phases and smooth velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If basic filtering operations are applied to measured data, then processing complexity is reduced, but velocity estimation accuracy deteriorates due to unconditional propagation of low velocities
Solution Approach 1:
The patent applies different filtering operations to different spatial locations based on local traffic conditions. Specifically, low velocities are only propagated upstream (against traffic flow) rather than unconditionally in both directions. This localized approach allows the system to maintain low processing complexity while improving velocity estimation accuracy by preventing the erroneous propagation of congestion velocities to upstream free-flowing traffic.
2Measurement precision
If FCD is used for traffic flow estimation, then spatial resolution is improved, but data sparsity worsens due to limited availability over the vehicle network
Solution Approach 1:
The patent introduces traffic phases as an intermediary classification layer between raw FCD measurements and velocity estimation. By classifying traffic into distinct phases (free-flow, synchronized flow, wide moving jam), the system can interpolate and estimate velocities in data-sparse regions based on the prevailing traffic phase characteristics. This intermediary classification enables the system to maintain high spatial resolution while compensating for data sparsity through phase-based inference.
3Measurement precision
If traffic state data is required in addition to FCD, then estimation accuracy is improved, but data acquisition complexity increases
Solution Approach 1:
The patent enables the system to estimate traffic state variables (velocity, flow, density) self-sufficiently using only FCD measurements. By deriving these variables directly from trajectory data through phase classification and filtering operations, the system eliminates the need for additional traffic state data from external sensors. This self-service approach maintains estimation accuracy while significantly reducing data acquisition complexity and infrastructure requirements.
Data Source
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AI summary
The present invention relates to a computer system for calculating accurate estimations of traffic velocity in a road network. The system comprises a plurality of devices, each device operable to deliver speed measurement data corresponding to the speed of a traffic flow in the road network. The system further comprises at least one computational unit, wherein each of the plurality of devices sends speed measurement data to the computational unit. The computational unit is operable to calculate accurate estimations of traffic velocity in the road network for specific regions by identifying regions with varying sizes and moving boundaries under the condition that within each region, a corresponding traffic phase p is constant, wherein each traffic phase is one of a free-flow phase, a synchronized phase or a wide moving jam phase; and calculating accurate estimations of traffic velocity in said regions based on the corresponding traffic phases.