Intelligent Gap Placement for Mobility Data Junctions
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Solution Overview
Problem
Traditional map-matching processes for vehicles and mobile devices are resource-intensive, latency-prone, and raise privacy concerns, while existing privacy-enhancing algorithms compromise accuracy in mobility data for location-based services.
Innovation Solution
An apparatus and method that use processing circuitry to identify junction behavior in mobility data by applying intelligent gap placement and sub-trajectory generation based on features like latitude, longitude, speed, and timestamp, encoding anonymized data to enhance privacy and accuracy for real-time navigation and autonomous vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional map-matching processes are used to establish vehicle position relative to road network, then location accuracy is improved, but computational resource consumption increases and processing latency occurs
Solution Approach 1:
The system pre-identifies junction points in mobility data before map-matching operations. By detecting junction behavior patterns (left turn, right turn, straight) in advance using machine learning models, the system prepares data structures that facilitate faster subsequent map-matching, reducing real-time computational burden while maintaining location accuracy
Solution Approach 2:
The mobility data processing is segmented into distinct operational phases: junction point identification, gap placement, and map-matching. This segmentation allows the system to apply different processing strategies to different data portions, reducing overall computational complexity by handling only critical junction segments with full map-matching while using simplified processing for non-junction segments
2Measurement precision
If traditional map-matching processes are used to establish vehicle position, then location accuracy is improved, but processing time increases
Solution Approach 1:
Junction points and their behavioral characteristics are identified in advance using machine learning models trained on mobility data features. This preliminary identification creates a structured representation of critical locations that can be rapidly processed during real-time operations, significantly reducing map-matching latency while preserving accuracy at decision-critical junction points
Solution Approach 2:
The system applies different processing qualities to different spatial locations: full-precision map-matching is applied specifically at identified junction points where accuracy is critical for navigation decisions, while simplified processing is used for non-junction segments, thereby reducing overall processing time while maintaining accuracy where it matters most
3Measurement precision
If complete mobility data is collected and stored, then data accuracy for location-based services is improved, but user privacy is compromised
Solution Approach 1:
The system extracts and removes personally identifiable information from mobility data during the gap placement process. By separating identifying characteristics from the core mobility patterns and storing only anonymized representations, the system preserves data accuracy for location-based services while eliminating privacy risks associated with complete data retention
Solution Approach 2:
An intermediary anonymization layer is introduced between data collection and data storage/processing. This intermediary process applies gap placement algorithms that intentionally introduce controlled uncertainties and remove identifying features, acting as a mediator that preserves the utility of mobility data for navigation services while protecting user privacy by preventing direct traceability to individual users
Data Source
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AI summary
A method, apparatus and computer program product are provided in order to provide at least approximate real-time intelligent gap placement within mobility data using junctions inferred by features of the mobility data. In this regard, a data chunk associated with a sequence of location probe data points representative of travel of a vehicle along a portion of a road network is received. Additionally, junction behavior in the data chunk is identified based on one or more features for the sequence of location probe data points. Based on a status of a previous data chunk and a junction point that corresponds to a location probe data point immediately after a last probe data point in the junction, a gap placement or a sub-trajectory is applied in the sequence of location probe data points to generate at least a first subsequence and second subsequence of the location probe data points.