Context-Sensitive Road Speed Inference for Routing
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Solution Overview
Problem
Conventional route planning applications fail to provide context-dependent driving directions, as they assume constant road conditions independent of time, day, and user preferences, neglecting factors like rush hour, weather, and events, which significantly affect travel velocities.
Innovation Solution
A robust traffic system representation using a weighted graph with nodes for intersections and edges for road segments, where weights are based on travel velocity statistics, incorporating sensed data and predictive models to account for various contexts, including time, day, weather, and events, to compute optimal routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional route planning applications use constant road speed assumptions, then the system complexity is low and ease of operation is maintained, but the routing accuracy and adaptability to different contexts deteriorate
Solution Approach 1:
The patent implements dynamic road speed estimation by making the speed parameter variable based on contextual factors such as time of day, day of week, weather conditions, and traffic events. Instead of using fixed constant speeds, the system continuously adjusts speed estimates according to the current context, thereby improving routing accuracy while managing complexity through modular context processing
Solution Approach 2:
The system changes the speed parameter from a static value to a dynamic value that varies with contextual conditions. By introducing multiple parameters (time, weather, events) that influence the speed estimation, the system achieves more accurate routing calculations that reflect actual driving conditions without requiring complete real-time data for all road segments
2Adaptability or versatility
If route planning applications incorporate multiple contextual factors, then the adaptability and routing accuracy improve, but the data collection requirements and system complexity increase
Solution Approach 1:
The patent creates a universal context processing framework that handles multiple types of contextual information (time, weather, events, traffic) through a unified methodology. This multi-functional approach allows the system to adapt to various contexts using the same underlying infrastructure, reducing the need for separate data collection systems for each context type and managing data requirements efficiently
Solution Approach 2:
The system introduces an intermediary context processing layer that mediates between raw data sources and the routing algorithm. This intermediary layer aggregates and processes contextual information from multiple sources, transforming diverse data into standardized context parameters that the routing system can utilize, thereby reducing the direct data burden on the core routing functionality
3Measurement precision
If real-time sensed data is collected for all road segments, then the routing accuracy improves, but the loss of time for data collection and processing increases
Solution Approach 1:
The patent implements partial data collection by focusing sensing efforts on road segments where context-dependent speed variations are most significant or where data is missing. Instead of uniformly collecting data for all road segments, the system selectively applies sensing and data processing to critical segments, thereby improving speed estimation accuracy for key routes while minimizing the overall time and computational resources required for data collection and processing
Data Source
AI summary
Sensing, learning, inference, and route analysis methods are described that center on the development and use of models that predict road speeds. In use, the system includes a receiver component that receives a traffic system representation, the traffic system representation includes velocities for a plurality of road segments over different contexts. A predictive component analyzes the traffic system representation and automatically assigns velocities to road segments within the traffic system representation, thereby providing more realistic velocities for different contexts where only statistics and/or posted speed limits were available before. The predictive component makes predictions about velocities for road segments at a current time or at specified times in the future by considering available velocity information as well as such information as the properties of roads, geometric relationships among roads of different types, proximal terrain and businesses, and other resources near road segments, and/or contextual information.