Traffic Speed Prediction Using Probe Vehicle Volume Data
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Solution Overview
Problem
Existing traffic speed prediction devices degrade in performance due to the lack of consideration for traffic volume data from probe vehicles that do not pass through collection points, leading to a decreased correlation with future traffic speed predictions.
Innovation Solution
A traffic speed prediction device that includes a communication module, memory, and processor to receive probe data, calculate remaining and exiting traffic volumes, and use these volumes along with traffic speed data to predict future traffic speeds by determining target road sections and learning weights to reduce mean squared error in predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only traffic volume data from probe vehicles passing through collection points is used, then the data collection process is simple, but the correlation with future traffic speed decreases and prediction performance degrades
Solution Approach 1:
The patent segments the traffic monitoring system into two parts: collection points with fixed infrastructure and probe vehicles with onboard sensors. By dividing the data collection responsibility, the system can gather comprehensive traffic volume data from multiple sources without requiring complex infrastructure at every location, thus improving prediction accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent makes probe vehicles serve multiple functions: they act as both transportation vehicles and mobile sensing units. The onboard sensors in probe vehicles collect traffic volume data that can be used for prediction, transforming ordinary vehicles into multi-functional elements that contribute to the overall system effectiveness without adding dedicated infrastructure.
2Reliability
If traffic volume data from all probe vehicles is collected and processed, then the correlation with future traffic speed improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary traffic volume data from probe vehicles that pass through or near the target road section, rather than processing all data from all probe vehicles. This selective extraction approach maintains high prediction accuracy by focusing on relevant data while reducing the overall processing burden.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives data from multiple probe vehicles, filters and aggregates the relevant traffic volume information, and then feeds it to the prediction model. This intermediary layer simplifies the complexity by handling data aggregation and filtering centrally, allowing individual probe vehicles to remain simple data sources.
3Reliability
If probe data from multiple road sections is used to calculate remaining traffic volume, then the prediction reliability improves, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary calculations of traffic volume data from probe vehicles passing through previous road sections before the target section. By pre-processing and storing this data in an organized manner, the system reduces the complexity of real-time calculations needed for predicting remaining traffic volume at the target section, while maintaining high prediction reliability.
Data Source
AI summary
In an embodiment a traffic speed prediction device includes at least one processor is configured to determine a second section connected with a first section, wherein the second section is a target road section, and wherein the first section includes a road section in front of the second section, to output first output data using traffic speed data during a first time, the traffic speed data being obtained based on probe data collected in the first section and the second section, to output second output data using traffic volume data during a second time, the traffic volume data being obtained based on the probe data collected in the first section and the second section, and to predict a traffic speed of the road including the second section using the first output data and the second output data.