Sign Placement Calculation Using Probe Data Destination Profiling
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Solution Overview
Problem
Current methods for sign placement on roadways are inefficient as they lack a systematic approach to determine optimal locations based on destination profiling from probe data, leading to suboptimal placement and reduced effectiveness in guiding drivers.
Innovation Solution
A method and apparatus that utilize probe data from sensors to identify trips, map match them to road segments, compare sign placement road segments to trip segments, and output destinations for informed sign placement, incorporating algorithms to calculate influence values for optimal sign placement locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used for sign placement, then the process is simple, but the placement effectiveness is reduced due to lack of systematic destination profiling
Solution Approach 1:
The system performs preliminary actions by collecting probe data, identifying trips, and profiling destinations before sign placement decisions are made. This advance preparation creates a systematic foundation for determining optimal sign locations based on actual travel patterns and destination preferences, thereby improving placement effectiveness while managing complexity through structured preprocessing.
Solution Approach 2:
The system enables self-service by automatically analyzing probe data, identifying trips, matching road segments, and determining destinations without requiring manual intervention. This automated destination profiling process improves sign placement effectiveness by using actual travel behavior data, while the systematic automation helps manage the inherent complexity of the analysis process.
2Measurement precision
If probe data analysis is implemented for sign placement, then destination identification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system applies segmentation by dividing the probe data analysis into distinct components: trip identification, road segment matching, and destination determination. This structured breakdown improves destination identification accuracy by systematically processing each aspect separately, while managing data processing complexity through modular organization of the analysis steps.
Solution Approach 2:
The system uses an intermediary approach by introducing trip profiles and road segment matches as intermediate structures between raw probe data and final destination identification. These intermediate representations improve measurement precision by providing structured data for analysis, while managing complexity by organizing raw data into manageable intermediate forms before final destination determination.
3Productivity
If systematic destination profiling is used, then sign placement optimization is improved, but computational requirements increase
Solution Approach 1:
The system implements partial action by focusing computational resources on analyzing only the relevant portions of probe data that contribute to destination identification and sign placement optimization. Rather than processing all possible data, the system selectively analyzes trips and road segments that directly impact sign placement decisions, thereby improving productivity while managing computational energy consumption.
Data Source
AI summary
Apparatus and methods are described for identification of a geographic location for sign placement. Probe data is received for a geographic area, and the probe data is collected by one or more sensors. Trips including destinations within the geographic area are matched with road segments from a geographic database. At least one potential sign placement road segment is compared to road segments of the trip. One or more destinations are selected from the trips based on the comparison of the at least one sign placement road segment to the trip road segments.