Intersection Turning Movement Identification Using Connected Vehicle Trajectories
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Solution Overview
Problem
Current methods for automatically identifying vehicle turning movements at intersections are costly and inefficient, particularly when scaling to hundreds or thousands of signalized intersections, as they often require additional geofencing and manual data collection, and struggle with accurately analyzing shared lanes and complex traffic patterns.
Innovation Solution
A method that uses connected vehicle data to automatically identify turning movements by establishing a center point for a traffic intersection, retrieving and filtering vehicle trajectory waypoints, and generating directional groups based on entry and exit headings, allowing for the reallocation of green time to reduce congestion and minimize stops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual vehicle turning movement counts are used, then data collection is simple, but it represents a significant cost for governmental agencies and is not scalable to hundreds or thousands of intersections
Solution Approach 1:
The system uses existing connected vehicle trajectory data that vehicles self-report through their onboard systems. The data collection process is automated, with vehicles effectively serving themselves by providing their own trajectory information without requiring manual intervention at each intersection.
Solution Approach 2:
The methodology uses a universal approach that can be applied to any signalized intersection without requiring intersection-specific equipment installation. The same trajectory data processing methodology works across hundreds or thousands of different intersections, making the system highly scalable.
2Extent of automation
If inductive loop detection or radar technology is used, then automatic turning movement counts are obtained, but additional detection equipment must be installed at each intersection increasing cost and complexity
Solution Approach 1:
The patent uses connected vehicle trajectory data as an intermediary medium to obtain turning movement information. Instead of installing detection equipment at intersections, the system processes trajectory data from vehicles that already carry this information, eliminating the need for physical detection infrastructure at each location.
Solution Approach 2:
The methodology replaces mechanical detection systems (inductive loops, radar) with a data processing approach. Instead of using physical sensors to detect vehicle movements, the system uses computational methods to analyze existing trajectory data, substituting mechanical detection with information processing.
3Extent of automation
If map matching or geofencing techniques are used to identify turning movements, then automatic data collection is achieved, but these techniques present significant challenges when scaling to hundreds or thousands of signalized intersections
Solution Approach 1:
The methodology segments the trajectory data processing into distinct steps: identifying entry and exit points, determining turning direction, and classifying movement types. This segmented approach makes the system more adaptable and easier to scale across multiple intersections compared to monolithic geofencing techniques.
Solution Approach 2:
The system changes the parameter space by using entry-exit point pairs and heading angle calculations instead of fixed geofence boundaries. This parameter transformation makes the methodology more versatile and scalable, as it doesn't require pre-defined geographic zones for each intersection.
4Productivity
If green time allocation is optimized based on turning movement analysis, then congestion is reduced and vehicle stops are minimized, but accurate identification of turning movements is required which current methods fail to provide at scale
Solution Approach 1:
The system uses trajectory data that provides feedback on actual vehicle paths and turning behaviors. By analyzing entry-exit point relationships and heading changes, the system obtains precise measurements of turning movements that can directly inform green time allocation decisions, improving signal performance.
Data Source
AI summary
A method of generating an output movement layout for a traffic intersection, includes receiving intersection geographical data, establishing a center point for the intersection, receiving connected vehicle (CV) data from vehicles approaching and within the traffic intersection based on a predetermined distance from the center point, establishing an area of interest for the intersection based on the received CV data, establishing entry and exit headings for each vehicle based on the CV data, generating directional groups based on the entry and exit headings of each vehicle, and generating an output movement layout for the intersection based on the generated directional groups.