Road Segment Traffic Tendency Using Downstream Speed Patterns
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Solution Overview
Problem
Traffic estimation systems fail to accurately predict traffic condition changes during dynamic events such as the formation or dissolving of traffic jams, relying solely on current vehicle speeds and experiencing high latency in status reporting.
Innovation Solution
Generate a current traffic speed pattern (TSP) data object for an initial vehicle location and a future TSP data object for an estimated downstream location, using machine learning models to estimate road segment traffic tendency based on these objects, providing notifications to vehicles for more accurate and efficient traffic condition predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic estimation systems rely solely on current vehicle speeds, then the system complexity is low, but the measurement precision of traffic condition predictions deteriorates
Solution Approach 1:
The system performs preliminary actions by generating future TSP data objects that predict traffic conditions at downstream locations before vehicles actually reach them. This allows the system to anticipate traffic changes (like jams forming or dissolving) rather than just reacting to current speeds, thereby improving prediction accuracy while maintaining manageable system complexity through structured data processing
Solution Approach 2:
The patent introduces a temporal dimension to traffic estimation by comparing current TSP data with future TSP data at different time horizons and spatial locations. This multi-dimensional approach (current vs. future, upstream vs. downstream) enhances measurement precision by capturing traffic dynamics over time and space, rather than relying on a single static speed measurement
2Reliability
If traffic estimation systems use only current speed data, then the computational resource use is low, but the reliability of traffic condition estimations deteriorates during dynamic events
Solution Approach 1:
The system generates future TSP data objects in advance that predict traffic conditions at downstream locations. This preliminary action allows the system to identify emerging traffic patterns (such as forming jams) before they fully develop, improving reliability during dynamic events while optimizing computational resources by focusing processing on predictive modeling rather than continuous real-time analysis of all current speeds
3Speed
If traffic estimation systems rely on current vehicle speeds only, then the latency in status reporting is low, but the speed of accurate traffic condition detection deteriorates
Solution Approach 1:
The system performs preliminary analysis by generating future TSP data objects that predict traffic conditions before they fully manifest. This allows the system to detect traffic changes (such as jam formation or dissolution) in advance, improving detection speed while managing latency by providing early warnings rather than waiting for current speed data to fully reflect the traffic condition change
Data Source
AI summary
A method, apparatus and computer program product are provided to estimate a road segment traffic tendency determination value. A current traffic speed pattern data object may be generated for an initial location of a vehicle and a future traffic speed pattern data object may be generated for an estimated downstream location of the vehicle. A road segment traffic tendency determination value may then be estimated based at least in part on the current traffic speed pattern data object and the future traffic speed pattern data object. A road segment traffic tendency notification may be provided to the vehicle.