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, limiting the accuracy and security of mobility data used in location-based services such as autonomous vehicle navigation and traffic management.
Innovation Solution
An apparatus and method that uses intelligent gap placement within mobility data, inferred by features such as latitude, longitude, speed, and timestamp, to anonymize data by applying machine learning or deterministic models to classify junction behavior and generate sub-sequences of location probe data points, enhancing privacy and accuracy while reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional map-matching processes are used to process mobility data, then location accuracy can be achieved, but computational resources are consumed and latency increases
Solution Approach 1:
The patent segments the continuous mobility data stream into discrete location probe data points and further divides the processing into identification phase (detecting junction behavior patterns) and processing phase (applying gap placement). This segmentation allows the system to process only critical portions of data intensively while handling other portions with simpler methods, improving overall computational efficiency while maintaining location accuracy through targeted map-matching at junction points.
Solution Approach 2:
The patent applies preliminary action by pre-identifying junction behavior patterns in mobility data before performing resource-intensive map-matching operations. The system detects and flags junction points in advance using lighter computational methods, then prepares appropriate gap placement strategies beforehand. This preliminary identification reduces the computational burden during real-time processing by pre-characterizing complex junction scenarios.
2Measurement precision
If traditional map-matching processes are used to process mobility data, then location accuracy can be achieved, but processing latency increases
Solution Approach 1:
The patent segments processing into rapid identification of junction behavior patterns followed by targeted map-matching only at critical points. By dividing the continuous data stream into discrete location probes and identifying junction points through pattern recognition rather than full map-matching, the system reduces processing latency while maintaining accuracy where it matters most at junction transitions.
Solution Approach 2:
The patent applies skipping by applying gap placement strategies that allow the system to rush through computationally intensive map-matching operations selectively. Instead of performing detailed map-matching at every location probe, the system skips intensive processing for non-junction segments and rushes through rapid gap placement at identified junction points, reducing overall latency while preserving accuracy at critical locations.
3Measurement precision
If complete mobility data is stored and processed, then navigation accuracy is improved, but privacy risks increase
Solution Approach 1:
The patent extracts and removes identifying information from mobility data by applying gap placement at junction points. The system extracts only the essential navigation-relevant features (junction behavior patterns, location sequences) while taking out or anonymizing data that could identify individual users or vehicles. This extraction process maintains navigation accuracy through preserved spatial-temporal patterns while eliminating privacy risks associated with complete mobility tracking.
Solution Approach 2:
The patent creates anonymized copies of mobility data that preserve navigation-relevant patterns without containing identifying information. By generating synthetic or generalized representations of mobility trajectories that maintain the statistical and spatial characteristics needed for accurate navigation analysis, the system provides navigation accuracy equivalent to complete data while eliminating privacy risks through the use of anonymized copies instead of raw personal data.
4Object-affected harmful factors
If gap placement is applied at every location probe, then privacy is improved, but data utility for navigation is reduced
Solution Approach 1:
The patent applies local quality by implementing gap placement selectively at junction points rather than uniformly across all location probes. The system identifies regions of high privacy risk (junction transitions where behavior patterns reveal sensitive information) and applies gap placement locally at these specific points, while maintaining continuous data elsewhere. This localized approach preserves navigation utility by keeping most data intact while providing privacy protection where it is most needed.
Solution Approach 2:
The patent applies partial action by implementing gap placement only at critical junction points rather than excessive gap placement at every location probe. The system identifies and applies gap placement selectively to the portion of data that provides the highest privacy benefit (junction behavior patterns), accepting that not all data points receive gap treatment. This partial application maintains navigation utility by preserving most of the mobility data while achieving sufficient privacy protection at critical points.
Data Source
AI summary
A method, apparatus and computer program product are provided in order to provide intelligent gap placement within mobility data using junctions inferred by features of the mobility data. In this regard, a sequence of location probe data points representative of travel of a vehicle along a portion of a road network during an interval of time is received. Additionally, junction behavior in the sequence of location probe data points is identified based on one or more features for the sequence of location probe data points. Based on a junction point that corresponds to a location probe data point immediately after a last probe data point in the junction, a gap placement in the sequence of location probe data points to generate at least a first subsequence of the location probe data points and a second subsequence of the location probe data points.