On-Demand Bus Pickup Selection Using Crowd Prediction
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Solution Overview
Problem
On-demand buses face challenges in ensuring smooth boarding due to potential crowding at dynamically determined pick-up locations, making it difficult for users to find and access these locations.
Innovation Solution
An information processing apparatus determines a non-crowded pick-up location based on user preferences and crowd prediction data, adjusting the pick-up location if necessary using cameras and signage to guide users to less crowded areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a dynamic pick-up location is determined for on-demand bus service, then operational flexibility and efficiency are improved, but the pick-up location may become crowded making it difficult for users to find and access
Solution Approach 1:
The system uses cameras to capture images at pick-up locations and processes these images to determine crowd levels. This feedback mechanism allows the system to monitor the actual state at pick-up locations and make adjustments based on real-time conditions, resolving the contradiction between operational efficiency and user accessibility by dynamically selecting locations that are both efficient and accessible.
Solution Approach 2:
The system predicts crowd levels at potential pick-up locations before finalizing the selection. By performing preliminary crowd level assessment using image processing and prediction models, the system can pre-identify suitable pick-up locations that are likely to be less crowded, thereby ensuring both operational efficiency and user accessibility from the outset.
2Ease of operation
If crowd prediction is used to select pick-up locations, then user experience is improved, but system complexity increases due to additional sensors and processing
Solution Approach 1:
The system uses existing infrastructure (cameras) to perform crowd level assessment rather than deploying specialized sensors. By leveraging already-available imaging devices and applying image processing algorithms, the system achieves crowd prediction functionality without significantly increasing hardware complexity, thus improving user experience while maintaining reasonable system complexity.
3Ease of operation
If pick-up locations are adjusted in real-time based on crowd levels, then boarding smoothness is improved, but response time and processing requirements increase
Solution Approach 1:
The system implements crowd level assessment at selective checkpoints rather than continuously monitoring all potential pick-up locations at all times. By using image processing to evaluate crowd levels at key moments and locations, the system achieves smooth boarding functionality while minimizing the time and computational resources required for real-time adjustments.
Data Source
AI summary
An information processing apparatus autonomously determines a pick-up location for an on-demand bus. The information processing apparatus has a controller configured to determine a first location that is not predicted to be crowded with people as the pick-up location based on a user's preferred pick-up location and first information related to locations within the operation area of the on-demand bus that are predicted to be crowded with people. The controller of the information processing apparatus selects the first location thus determined as the pick-up location for the on-demand bus.