Traffic Pattern Alert System Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Navigation platforms fail to generate exact and accurate maps due to outdated traffic data, leading to user problems during travel, as the data is not updated regularly to reflect changes in road construction and blockages.
Innovation Solution
A system and method that receive and analyze historic and current traffic data using data analytics and machine learning algorithms to identify changes in traffic patterns, determine the root cause of these changes, and generate alerts for specific road segments, enabling real-time updates and recommendations for road infrastructure planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is updated after a predefined time interval, then data storage and processing is simplified, but navigation accuracy deteriorates due to outdated traffic data
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing traffic data in the background before navigation queries are made. Traffic pattern changes are detected and stored in advance, so when navigation needs accurate data, it is already available without requiring complex real-time update mechanisms during actual navigation operations.
Solution Approach 2:
The system implements feedback by continuously monitoring traffic data and comparing current patterns with historical data. When changes are detected, the system automatically triggers data updates and notifications. This closed-loop feedback mechanism ensures navigation accuracy is maintained without requiring manual intervention or complex scheduling systems.
2Device complexity
If traffic data is not updated regularly, then data processing complexity is reduced, but map accuracy deteriorates and user problems increase
Solution Approach 1:
The system employs self-service mechanisms where traffic data automatically updates itself through continuous collection and analysis. The system monitors its own data quality, detects when updates are needed, and performs updates autonomously without external intervention. This maintains map accuracy while keeping the processing system relatively simple through automated self-maintenance.
Solution Approach 2:
The data processing system transitions from static periodic updates to dynamic continuous monitoring. The system adaptively adjusts its processing intensity based on detected traffic pattern changes, performing intensive analysis only when necessary. This dynamic approach maintains reliability while avoiding unnecessary processing complexity during stable conditions.
3Measurement precision
If historic and current traffic data is analyzed using data analytics and machine learning, then traffic pattern detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational system is segmented into distinct functional modules: data collection, historical data storage, pattern analysis, and update execution. Each module handles specific tasks independently, reducing overall system complexity. The machine learning algorithms are applied in discrete analysis steps rather than as a monolithic complex system, making the computational process more manageable and maintainable.
Data Source
AI summary
The present disclosure relates to system(s) and method(s) for generating an alert based on change in traffic pattern. The system receives historic traffic data and current traffic data, associated with each road segment, from a set of road segments. Further, the system identifies a change traffic pattern based on analysing the historic traffic pattern and the current traffic pattern, using data analytics and a machine learning algorithm. Furthermore, the system identifies a sub-set of road segments, from the set of road segments, based on comparison of the change in traffic pattern and a pre-defined threshold. The system further determines root cause of change in traffic pattern by analysing the sub-set of road segments. Further, the system generates an alert for updating one or more road segments, from the sub-set of road segments, based on the root cause of change in traffic pattern.