Transit Schedule Adaptation via Real-Time Position Data
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Solution Overview
Problem
Public transit systems face challenges in maintaining accurate schedules, especially during peak traffic, weather conditions, and passenger load, leading to uncertainty for passengers waiting at transit stops, and requiring significant manual effort to update arrival and departure times.
Innovation Solution
A self-learning transit system that collects and calculates real-time data on transit stop arrival and departure times, creating a flexible schedule without manual input, using a control center and transit vehicle unit to provide accurate forecasts to passengers through wireless communication, adapting to conditions like rush hour, weather, and holidays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed written schedule is used for transit vehicles, then the schedule can be published and distributed to passengers, but the schedule cannot adapt to changing traffic conditions, weather, and operational variables
Solution Approach 1:
The patent implements a dynamic schedule system that automatically adjusts arrival and departure times based on real-time data from GPS tracking, traffic conditions, and historical performance. The system continuously updates the schedule without manual intervention, allowing the transit system to adapt to changing conditions while maintaining reliable and accurate timing information for passengers.
Solution Approach 2:
The system performs self-learning by automatically collecting position information, calculating medial time tables, and updating schedules without human input. The control center autonomously processes data from multiple sources and adjusts the schedule based on learned patterns and real-time conditions, eliminating the need for manual schedule updates while improving both adaptability and reliability.
2Reliability
If manual updates are performed to adjust arrival and departure times, then the schedule can be kept current, but significant time and effort are required
Solution Approach 1:
The system automatically collects position information from GPS devices on transit vehicles, stores the data, calculates medial time tables, and updates the schedule without any manual intervention. This self-service approach eliminates the time and effort previously required for manual schedule updates while maintaining high schedule accuracy through continuous automated adjustments.
Solution Approach 2:
The patent replaces the manual mechanical process of schedule updating with an automated electronic system. The control center uses computer algorithms to process GPS data, calculate arrival and departure times, and update the schedule automatically, substituting human labor with an efficient automated computing system that operates continuously without fatigue or error.
3Extent of automation
If a self-learning system is implemented to automatically calculate schedules, then manual effort is eliminated, but the system complexity increases
Solution Approach 1:
The control center serves multiple functions within a single integrated system: it collects position information from GPS devices, stores data in databases, calculates medial time tables using learned patterns, updates schedules, and communicates with transit vehicles and passengers. This multi-functional approach consolidates what could be separate complex systems into one unified platform, reducing overall system complexity while achieving high automation.
Solution Approach 2:
The system implements continuous feedback loops where position information from GPS tracking is fed back to the control center, which updates the schedule based on actual performance and learned patterns. This feedback mechanism allows the system to self-correct and improve over time without manual intervention, achieving high automation through a relatively simple closed-loop control structure rather than complex open-loop systems.
Data Source
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AI summary
A system and method is provided that collects data including transit vehicle arrival and departure data sent from the transit vehicle to a stationary control center. The stationary control center continuously calculates, using the data provided, a medial time table, a medial travel time to a destination on a transit route and a medial travel time between transit stops on the route. The medial values may be calculated differently for different conditions, such as for rush hour, holiday, or certain weather conditions to increase the accuracy of the schedule forecast. Actual position and time information of a transit vehicle may be transmitted via wireless communication from a transit vehicle mounted device to the stationary control center. Such transmission of actual position and time information is transmitted e.g. when the transit vehicle arrives and departs each transit stop on the transit route. The medial values can show on different kinds of displays inside the vehicle, stationary at the stops and via the internet.