Passenger Seat Occupancy Prediction for Congestion-Free Boarding
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Solution Overview
Problem
At public transport stops, passenger flows often impede each other due to uneven distribution of boarding and alighting passengers, and boarding passengers lack information about available seats, leading to congestion.
Innovation Solution
A computer-implemented method predicts the future state of occupancy of passenger seats by determining the actual state using sensors, acquiring associated passenger data, and outputting prediction data to passengers via output apparatuses, enabling efficient distribution and route guidance to minimize encounters and optimize boarding and alighting processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If boarding passengers are directed to sections with free seats without prediction information, then passengers may board efficiently, but passengers search for seats causing encounters and congestion
Solution Approach 1:
The system performs preliminary actions by predicting future seat occupancy states before passengers board, and providing this information to passengers in advance. This allows passengers to make informed decisions about where to board and search for seats, preventing congestion and encounters that would otherwise occur during the boarding process.
Solution Approach 2:
The system establishes a feedback loop where sensor data about current occupancy is continuously collected, processed through prediction algorithms, and the resulting prediction information is provided back to passengers. This feedback enables passengers to adjust their boarding behavior dynamically, reducing encounters and improving overall boarding efficiency.
2Ease of operation
If prediction information is provided to passengers, then passenger flow is optimized and encounters reduced, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by combining sensor data collection, occupancy detection, future state prediction, and information provision to passengers within a single integrated framework. This universal approach handles multiple tasks (monitoring, predicting, communicating) without requiring separate complex systems for each function.
Solution Approach 2:
The prediction system operates autonomously by automatically collecting sensor data, processing it through prediction algorithms, and providing information to passengers without requiring manual intervention. This self-service capability reduces operational complexity while maintaining ease of use for passengers.
3Device complexity
If current occupancy data is used without future prediction, then system is simple, but cannot prevent seat searches and congestion
Solution Approach 1:
The system performs preliminary prediction of future occupancy states based on current sensor data and historical patterns. This advance prediction capability allows the system to provide actionable information to passengers before they board, enabling them to avoid sections that will become congested, thereby improving passenger flow efficiency without requiring overly complex real-time control mechanisms.
Data Source
AI summary
A method for predicting an occupancy state of a passenger seat and for traffic management of passengers, includes determining an actual occupancy state of the passenger seat by using sensors, acquiring information associated with the future occupancy state of the passenger seat, determining prediction data relating to a future occupancy state of the passenger seat on the basis of the actual occupancy state and the associated information by using a data processing apparatus, and outputting the prediction data to passengers by using an output apparatus. A route guidance for passengers is determined on the basis of the prediction data, and the route guidance is output to passengers by using an output apparatus. A system for carrying out the method is also provided.