Traffic Light Cycle Estimation Using Multi-Directional Vehicle Start Times
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing traffic-light cycle length estimation devices fail to accurately estimate cycle length at intersections with light traffic, as they assume the presence of a moving vehicle when the light turns green.
Innovation Solution
A traffic-light cycle length estimation device that acquires start times of vehicles in multiple directions, calculates time differences, and uses relative frequency and greatest common divisor methods to estimate cycle length, even in low-traffic conditions, by combining data from multiple start directions and using the start time of the first vehicle to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the device uses start times of vehicles in a single direction to estimate cycle length, then the estimation is simple, but the estimation cannot be performed at intersections with light traffic
Solution Approach 1:
The patent combines start time data from multiple directions (first direction and second direction) at the intersection to increase the number of available sampling points. By merging data from multiple directions, the system can perform reliable cycle length estimation even when traffic volume is low, as the combined data provides sufficient samples for accurate statistical analysis.
Solution Approach 2:
The patent transitions from single-direction observation to multi-directional observation, adding a spatial dimension to the data collection. By observing vehicle start times from multiple directions around the intersection, the system increases the dimensionality of the sampling space, ensuring adequate data availability for estimation.
2Adaptability or versatility
If the device uses start times from multiple directions to estimate cycle length, then the estimation can be performed at light traffic intersections, but the data processing complexity increases
Solution Approach 1:
The patent replaces complex manual or rule-based analysis with automated statistical processing. By using relative frequency analysis and greatest common divisor calculations on the collected start time data, the system automatically determines the cycle length without requiring complex judgment logic, thus managing processing complexity through systematic mathematical methods.
Solution Approach 2:
The system automatically processes the multi-directional start time data using statistical methods to identify the cycle length. The data itself provides the information needed for estimation through frequency analysis and GCD calculation, allowing the system to self-determine the cycle length without external intervention or complex preprocessing.
3Device complexity
If the device assumes a vehicle is always present when the light turns green, then the estimation method is simple, but the estimation fails at intersections with light traffic
Solution Approach 1:
Instead of requiring exactly one vehicle per direction (partial action), the system collects start time data from multiple vehicles across multiple directions (excessive action). This over-sampling approach ensures that even if some directions have no vehicles or only one vehicle, there are still sufficient data points from other directions to perform reliable cycle length estimation.
Solution Approach 2:
The patent changes the fundamental parameter assumption from 'one vehicle per direction' to 'multiple vehicles across multiple directions'. By transforming the data collection parameter from a strict per-direction requirement to a multi-directional aggregate, the system maintains estimation reliability under varying traffic conditions.
4Measurement precision
If the device collects start times from multiple directions, then the number of samplings increases for better estimation, but the time and resources required for data collection increase
Solution Approach 1:
The system continuously collects start time data from multiple directions simultaneously as vehicles naturally pass through the intersection. Rather than conducting separate measurements, the data collection occurs continuously and concurrently across all monitored directions, maximizing sampling efficiency without extending the overall measurement period.
Solution Approach 2:
The system pre-establishes monitoring points at multiple directions before traffic arrives, so that when vehicles pass through, their start times are automatically recorded. This preliminary setup allows the system to immediately capture data from multiple directions without sequential setup time, reducing the total time required for comprehensive data collection.
Data Source
AI summary
For each of the traveling directions (traveling direction 1 to traveling direction 4) at an intersection, a traffic-light cycle length estimation device acquires a time at which a vehicle in the stopped state starts moving, calculates the time difference between neighboring start times, which have been acquired, as a start interval, and generates a histogram based on the number of samplings of start intervals. The device combines the generated histograms into a histogram for all direction to generate one histogram that represents the relation between the start intervals and the number of samplings and, based on this histogram, estimates the cycle length of the traffic light. If a particular value, one of the start intervals, corresponds to the maximum number of samplings, that particular value is estimated as the cycle length.