Road Condition Prediction Model Using Adjacent Segment Data
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Solution Overview
Problem
Existing methods for obtaining real-time road condition information are inaccurate for sparse road segments due to insufficient data, leading to unreliable traffic flow calculations and misjudgment of road conditions.
Innovation Solution
A method that collects and combines real-time driving data from multiple vehicles, including first and second vehicles passing adjacent road segments, using a trained road-condition prediction model to generate accurate road condition information, covering all road segments and expanding data coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GPS positioning information of vehicles is collected to calculate real-time speed on each road segment, then the method is simple and straightforward, but for short-distance or sparsely-traveled road sections with less vehicle data, the calculation of traffic flow becomes unreliable and accuracy is insufficient
Solution Approach 1:
The patent combines driving data from multiple sources including GPS positioning information, vehicle speed, acceleration, and road topology data from adjacent road segments. By merging these diverse data sources, the system achieves reliable road condition assessment even for sparsely-traveled sections where individual data sources would be insufficient
Solution Approach 2:
The patent introduces a trained road-condition prediction model as an intermediary that processes raw driving data and topology information to generate accurate road condition predictions. This model acts as a mediator that transforms incomplete or sparse data into reliable traffic flow calculations and road state determinations
2Reliability
If only vehicles currently passing the target road segment are used for data collection, then the data is specific to the target segment, but the data coverage is insufficient for accurate judgment
Solution Approach 1:
The patent extends data collection from the one-dimensional target road segment to multiple dimensions by including topology road segments (adjacent road segments within a target range). This dimensional expansion allows the system to leverage data from surrounding areas while maintaining focus on the target segment, thereby increasing data coverage without sacrificing specificity
Data Source
AI summary
A method includes: obtaining a number of vehicles passing a driving road segment within a duration; determining whether the driving road segment is a sparse road segment by determining whether the number of vehicles is less than or equal to a vehicle threshold; obtaining first real-time driving data transmitted by a first vehicle the target road segment, and obtaining first driving characteristic-information of the target road segment based on the first real-time driving data; obtaining second real-time driving data transmitted by a second vehicle passing a topology road segment, and obtaining second driving characteristic-information of the target road segment based on the second real-time driving data, the topology road segment being a road segment within a target range of the target road segment; and generating road-condition information of the target road based on at least the second driving characteristic-information.