Demand Forecasting for Mobility Units via User Group Segmentation
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Solution Overview
Problem
Existing systems lack an efficient method to distribute autonomous and human-assisted self-driving vehicles across a city to meet varying mobility demands based on user preferences and behavioral trends, which is crucial for providing effective transportation services.
Innovation Solution
A data-driven method that estimates mobility demand by modeling past data using deep learning architectures, determining user group activity preferences, and generating transportation need requests with specific coordinates and attributes, allowing for the efficient distribution of mobility units based on predicted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles share information and use complex algorithms for real-time decisions, then collision avoidance and route planning improve, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the city into multiple regions and divides the vehicle fleet into different groups operating in specific regions. Each vehicle operates with localized decision-making algorithms rather than requiring global coordination, reducing computational complexity while maintaining collision avoidance through regional information sharing.
Solution Approach 2:
The system performs preliminary distribution of vehicles to regions based on forecasted demand before peak periods occur. By pre-positioning vehicles in high-demand regions, the system reduces real-time computational burden for route planning and collision avoidance, as vehicles start from optimized initial positions rather than requiring complex real-time redistribution.
2Adaptability or versatility
If vehicles are distributed across the city to meet customer demand, then service coverage improves, but difficulty in optimizing distribution increases
Solution Approach 1:
The system forecasts mobility demand and performs preliminary vehicle distribution to regions before peak demand periods. By using historical data and machine learning models to predict future demand patterns, the system pre-positions vehicles in high-demand regions, simplifying real-time distribution decisions while maintaining broad service coverage.
Solution Approach 2:
The system implements region-specific distribution strategies tailored to local characteristics and demand patterns. Each region receives vehicles optimized for its specific needs rather than a uniform city-wide distribution, making the overall distribution system more adaptable while managing complexity through localized optimization.
3Measurement precision
If the system forecasts mobility demand using user group preferences and activities, then transportation demand prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments users into different user groups with distinct activity patterns and preferences. By analyzing data at the user group level rather than individually, the system achieves accurate demand prediction while reducing computational complexity through aggregation and pattern recognition at the group level.
Solution Approach 2:
The system uses machine learning models to create simplified representations (copies) of complex user behavior patterns. These models capture essential demand characteristics without requiring processing of every individual data point, maintaining prediction accuracy while reducing data processing complexity.
Data Source
AI summary
Predicting transportation demand in a predetermined area, based on estimating a present mobility demand and based on user group preferences. Generated transportation need requests include at least a time stamp, a pick-up coordinate, a drop-off coordinate, a user group indication, a pick-up venue category based on the pick-up coordinate, and a drop-off venue category based on the drop-off coordinate. A signal indicative of the transportation need request is provided.