Transit Gate Width Detection Using Multi-Sensor ML Fare Screening
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Solution Overview
Problem
Transit systems face challenges in accurately detecting valid riders versus fare evaders at exit gates due to restricted coverage, complex sensor integration, power consumption issues, and viewpoint variations, leading to revenue loss, security threats, and congestion during peak hours.
Innovation Solution
A width detection system using a sensor system with primary and secondary sensors and a machine learning engine to measure object width, determining valid riders by comparing distances and widths against predefined thresholds, and processing images to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are integrated to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into multiple functional sensor units (primary sensor, secondary sensor, third sensor) positioned at different locations. Each sensor handles specific detection tasks, and their results are combined to achieve high-precision width measurement while maintaining manageable system complexity through modular architecture
Solution Approach 2:
A processor acts as an intermediary that receives signals from multiple sensors, performs coordinate transformations, and integrates the data to determine object width. This intermediary component simplifies the overall system by centralizing the complex integration logic in a dedicated processing unit rather than requiring direct complex interconnections between all sensors
2Area of stationary object
If sensor coverage area is expanded to detect all objects, then detection coverage is improved, but use of energy increases
Solution Approach 1:
Different sensors are positioned to detect specific local regions (primary sensor for one side, secondary sensor for another side, third sensor for additional coverage). Each sensor operates independently with optimized local coverage, achieving comprehensive detection area while minimizing energy consumption by avoiding redundant sensing in all directions simultaneously
Solution Approach 2:
The system uses sensors positioned at different spatial locations (different dimensions) to achieve comprehensive coverage. By distributing sensors across multiple positions rather than using a single omnidirectional sensor, the system achieves expanded coverage area with lower power consumption per sensor unit
3Measurement precision
If threshold values are adjusted to improve detection accuracy, then measurement precision is improved, but ease of operation decreases
Solution Approach 1:
The processor automatically determines appropriate threshold values based on the measured width data and object characteristics. The system performs self-calibration and adaptive threshold adjustment without requiring manual configuration, thereby maintaining high detection accuracy while simplifying operation for end users
Solution Approach 2:
The system uses feedback from the detection results to automatically adjust threshold values. By continuously monitoring detection outcomes and refining threshold settings based on actual measurements, the system maintains high precision while eliminating the need for manual threshold configuration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively distinguishes between valid riders and fare evaders, reducing fare evasion, maintaining system revenue, and preventing congestion by accurately actuating gate paddles based on precise width and image analysis.
Implementation Method 1
The primary sensor emits a primary signal directed at the second gate cabinet and determines a primary distance of the object to the primary sensor based on the primary signal reflected from the object
Implementation Method 2
The secondary sensor emits a secondary signal directed at the first gate cabinet to determine a blocked state of the secondary sensor, a clear state of the secondary sensor, or a secondary distance of the object to the secondary sensor
Data Source
AI summary
A width detection system to measure a width of an object at a transit gate of a transit system is disclosed. The width detection system includes a gate paddle to control access through the transit gate, a first gate cabinet and a second gate cabinet of the transit gate separated by an aisle width, a sensor system, and a controller. The sensor system includes a primary sensor positioned at the first gate cabinet of the transit gate. The primary sensor emits a primary signal directed at the second gate cabinet and determines a primary distance of the object to the primary sensor. The controller determines the width of the object based on the primary distance and the aisle width, compares the width against a primary threshold, and actuates the gate paddle.