Traffic Density Analysis Using Mobile Device Data
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Solution Overview
Problem
Current transportation network management lacks effective data gathering and analysis, making it difficult for managers to determine where to make changes, such as upgrading systems or optimizing commute times, due to antiquated data collection methods.
Innovation Solution
A traffic routing and analysis system that uses data from individual cellular or mobile devices to determine traffic density within transportation networks, comparing historical data to real-time data to detect abnormalities and predict commute times, route selection, and policy analysis, accessible through an API for various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If antiquated data gathering methods are used, then device complexity is reduced, but measurement precision and reliability of traffic density data deteriorate
Solution Approach 1:
The patent uses mobile devices as intermediary data collection points, leveraging their existing sensors and processing capabilities to gather traffic density information without requiring complex dedicated infrastructure. The mobile devices act as mediators between the transportation network and the analysis system, providing accurate real-time data while keeping the overall system complexity manageable.
Solution Approach 2:
The system leverages the multi-functionality of mobile devices, which already serve multiple purposes (communication, navigation, etc.), to also perform traffic data collection. This universal approach eliminates the need for specialized single-function devices, reducing overall system complexity while maintaining high measurement precision through the use of existing sophisticated sensors.
2Productivity
If real-time data collection from individual devices is implemented, then productivity of transportation management is improved, but loss of information privacy increases
Solution Approach 1:
The system extracts only the necessary aggregated traffic density information from individual device data, separating the useful productivity-enhancing metrics from sensitive personal information. By taking out only the required data elements (location, time, device count) and leaving out personal identifiers, the system improves transportation management productivity while minimizing privacy loss.
Solution Approach 2:
The system uses an intermediary processing layer that aggregates and anonymizes data from individual devices before analysis. This intermediary step transforms personal location data into anonymous traffic density metrics, enabling productivity improvements through real-time analysis while protecting individual privacy through automatic anonymization.
3Reliability
If historical data is compared with real-time data to detect abnormalities, then reliability of traffic predictions is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical traffic data in structured formats before real-time analysis is needed. This preliminary organization of historical data allows for rapid comparison with real-time inputs, improving prediction reliability while minimizing the time required for actual abnormality detection when real data arrives.
Solution Approach 2:
The system applies partial action by focusing comparison efforts on only the most relevant historical data segments and key traffic parameters rather than analyzing all historical data comprehensively. This selective comparison approach maintains high prediction reliability by focusing on critical patterns while reducing overall data processing time through targeted analysis.
Data Source
AI summary
A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.