Traffic Data Filtering for Incident Prediction

Solution Overview

Problem

Traffic control centers face challenges in predicting and managing traffic incidents due to limitations in human resources and bandwidth constraints, which hinder effective monitoring and response to recurring and sudden capacity events like congestion and accidents.

Innovation Solution

A system that utilizes data analytics and machine learning techniques to predict traffic incidents by filtering and selecting relevant data from multiple sources, including sensors and weather data, to prioritize alerts and reduce network usage, allowing for early remedial actions and efficient resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If data from multiple sources is transmitted to traffic control centers, then information completeness is improved, but network bandwidth consumption increases

Engineering Contradiction:

Improveinformation completenessVSAvoidnetwork bandwidth consumption

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The system extracts and transmits only the most relevant data elements from multiple sources based on predicted incident causes. The filtering mechanism identifies and removes redundant or less critical data before transmission, ensuring information completeness for incident response while minimizing network bandwidth consumption by sending only essential data.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies different data transmission qualities to different data sources based on their relevance to predicted incidents. Critical data sources associated with high-probability incident causes receive higher transmission priority and more frequent updates, while less critical sources are filtered more aggressively, optimizing the balance between information completeness and bandwidth usage.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If more data sources are monitored, then incident detection accuracy is improved, but system complexity increases

Engineering Contradiction:

Improveincident detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts and processes only the most relevant data elements from multiple sources based on predicted incident causes. The filtering mechanism identifies and removes redundant or less critical data before transmission, ensuring information completeness for incident response while minimizing network bandwidth consumption by sending only essential data.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs preliminary filtering and prediction of incident causes before data transmission. By pre-identifying which data sources are most relevant to potential incidents, the system reduces the complexity of real-time monitoring while maintaining high detection accuracy, as the heavy lifting of data prioritization is done in advance.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If all data sources are transmitted, then response accuracy is improved, but response time increases due to processing load

Engineering Contradiction:

Improveresponse accuracyVSAvoidresponse time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary filtering and prediction of incident causes before data transmission. By pre-identifying which data sources are most relevant to potential incidents, the system reduces the complexity of real-time monitoring while maintaining high detection accuracy, as the heavy lifting of data prioritization is done in advance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies different data transmission qualities to different data sources based on their relevance to predicted incidents. Critical data sources associated with high-probability incident causes receive higher transmission priority and more frequent updates, while less critical sources are filtered more aggressively, optimizing the balance between information completeness and bandwidth usage.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10395183B2Real-time filtering of digital data sources for traffic control centers

Publication Date: 2019.08.27 NEC CORP

AI summary

A system for filtering data for a traffic control center includes: a plurality of data sources, comprising a plurality of traffic-related data sources and a weather-related data source; one or more network computing devices, configured to process data from the plurality of data sources to predict causes associated with predicted traffic incidents, and to select data from the plurality of data sources to be output to the traffic control center based on the predicted causes; and one or more output devices, located at the traffic control center, configured to display respective data selected by the one or more network computing devices.