Traffic Emissions Forecasting Using Clustered Regional Mobility Data
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Solution Overview
Problem
Conventional traffic forecasting methods fail to provide accurate, scalable, and context-aware seasonal predictions for CO2 emissions, especially in high-demand systems like Digital Twins, which require real-time responses and handle multiple regions simultaneously, and do not adequately consider complex patterns or anomalies.
Innovation Solution
A computer-implemented method that clusters historical traffic data by similarity, determines optimal parameters for seasonal forecasting, and generates combined forecasts by analyzing mobility flows and correlations between regions, with anomaly detection based on noise component deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional traffic forecasting methods are used, then the system is simple to operate, but the measurement precision of CO2 emissions predictions is insufficient
Solution Approach 1:
The patent segments the forecasting system into distinct functional modules: data collection module, clustering module, parameter determination module, forecasting module, and emissions calculation module. Each module handles a specific aspect of the forecasting process, allowing the system to achieve high measurement precision through specialized processing while maintaining operational clarity through modular architecture.
Solution Approach 2:
The patent introduces traffic data clustering as an intermediary step between raw traffic data and final emissions forecasts. By clustering similar traffic patterns and using representative datasets from each cluster, the system mediates the complex relationship between diverse traffic conditions and CO2 emissions, enabling accurate predictions without requiring direct complex modeling of all possible traffic scenarios.
2Productivity
If the system handles multiple regions simultaneously, then the productivity increases, but the device complexity increases
Solution Approach 1:
The patent implements a universal forecasting framework that can handle multiple geographical regions simultaneously through a single integrated system. The clustering module and parameter determination module are designed to process traffic data from any region using the same methodology, allowing the system to maintain consistent high-performance forecasting across diverse regions without requiring region-specific complex subsystems.
Solution Approach 2:
The patent merges the processing of multiple regions into a unified forecasting system that leverages cluster-level representative datasets. By combining data from multiple regions and identifying common patterns through clustering, the system achieves synergistic effects where the whole system performs better than individual region analyses would suggest, enabling simultaneous multi-region processing with manageable complexity.
3Speed
If real-time responses are required, then the speed increases, but the loss of information increases due to limited data availability
Solution Approach 1:
The patent performs preliminary actions by pre-processing and clustering traffic data in advance, creating ready-to-use representative datasets for each cluster. This preliminary organization of data allows the forecasting system to quickly generate emissions predictions in real-time without needing to process raw data from scratch, thus achieving fast response times while maintaining information completeness through the pre-established cluster representations.
Data Source
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AI summary
A computer-implemented method comprising: clustering sets of traffic data based on similarity to each other to generate a plurality of clusters of the sets of traffic data; selecting as a representative set of traffic data the set of traffic data of the cluster which is most similar to an average; and based on a representative set of traffic data corresponding to a target geographical region, performing mobility analysis to determine parameters for a seasonal traffic forecast and, based on at least one representative set of traffic data corresponding to at least one other geographical region, performing mobility analysis to determine parameters for at least one other seasonal traffic forecast for the at least one other geographical region; performing a traffic forecasting process for the target geographical region; and predicting emissions produced by traffic in the target geographical region based on the combined traffic forecast.