Traffic Forecast Decomposition for Emissions Anomaly Detection
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Solution Overview
Problem
Conventional methods for monitoring traffic and emissions lack seasonal and low-granularity predictions in real-time environments, fail to consider complex patterns, and are not context-aware, making it difficult to detect CO2 anomalies effectively, especially in Digital Twin (DT) solutions that manage multiple regions simultaneously.
Innovation Solution
A computer-implemented method that performs traffic forecasting using historical data to generate seasonal forecasts, analyzes mobility flow between regions, and detects anomalies by decomposing traffic forecasts into seasonal, trend, and noise components, allowing for context-aware and scalable emissions estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for traffic monitoring, then device complexity is reduced, but measurement precision and anomaly detection capability deteriorate
Solution Approach 1:
The patent segments the traffic forecast into three distinct components: seasonal component (capturing periodic patterns), trend component (capturing long-term direction), and noise component (capturing anomalies). This segmentation allows precise measurement of emissions by isolating anomalous deviations from normal seasonal and trend patterns, while managing complexity through modular decomposition of the forecasting task.
Solution Approach 2:
The patent introduces an intermediary decomposition process that acts as a mediator between raw traffic data and emissions monitoring. By inserting the seasonal-trend-noise decomposition as an intermediary layer, the system achieves high measurement precision for anomaly detection without directly confronting the full complexity of raw traffic patterns, as the intermediary structure organizes and simplifies the data analysis.
2Measurement precision
If seasonal forecasts are generated for multiple regions, then measurement precision improves, but computing resources and time increase
Solution Approach 1:
The patent applies segmentation by generating seasonal forecasts for multiple regions simultaneously through parallel processing of the decomposition algorithm. Each region's traffic data is independently decomposed into seasonal, trend, and noise components, allowing precise anomaly detection across all regions without sequential processing delays. This segmented approach maintains high measurement precision while reducing total computation time through concurrent execution.
Solution Approach 2:
The patent implements partial action by focusing computational resources only on the noise component for anomaly detection, rather than fully processing all three components (seasonal, trend, noise) with equal depth. By performing partial decomposition and primarily analyzing the noise component for anomalies, the system achieves sufficient measurement precision for emissions monitoring while significantly reducing computing time and resource requirements compared to complete multi-component analysis of all regions.
3Measurement precision
If traffic data from multiple regions is analyzed, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments multi-region traffic data analysis into standardized decomposition modules that process each region's data through the same seasonal-trend-noise framework. This segmentation creates a uniform processing pipeline that can handle multiple regions simultaneously, improving emissions estimation accuracy through aggregated regional analysis while managing data processing complexity through modular, repeatable processing steps that avoid ad-hoc complexity for each additional region.
Solution Approach 2:
The patent implements universality by designing a universal decomposition algorithm that functions across all geographic regions with the same processing logic. The seasonal-trend-noise decomposition model serves as a multi-functional framework that adapts to different regions without requiring region-specific customization, thereby improving emissions estimation accuracy through comprehensive multi-region coverage while minimizing data processing complexity through a single universal processing approach.
Data Source
AI summary
A computer-implemented method may include performing a traffic forecasting using traffic data of a first time period to generate a first traffic forecast for a target geographical region; performing the traffic forecasting using traffic data of a second other time period to generate a second traffic forecast for another geographical region; decomposing the first and second traffic forecast into components of seasonal, trend, and noise; comparing a first noise component of the first traffic forecast with a second noise component of the second traffic forecast to detect at least one anomaly, by comparing at least one deviation between the first and second noise components to an anomaly threshold. Emissions produced by traffic in the target geographical region may be predicted based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly.