Transportation Service Time Point Distribution Prediction
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Solution Overview
Problem
On-demand transportation services face challenges in efficiently meeting demand, particularly during peak and idle periods, as existing systems struggle to accurately predict transportation service time points, leading to inefficiencies in service distribution.
Innovation Solution
A system and method that utilize processors to determine a predicted distribution of future transportation service time points by analyzing historical service time points, applying variance calculations, and using correction coefficients to enhance prediction accuracy, allowing for more precise service frequency predictions and improved demand management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical service time points are analyzed using variance calculations, then prediction accuracy of transportation service time points is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary calculations of variance and standard deviation on historical service time points before making predictions. By pre-processing the historical data to extract statistical characteristics (variance, standard deviation), the system prepares prediction parameters in advance, which improves prediction accuracy while managing complexity through structured data preparation
Solution Approach 2:
The patent introduces statistical parameters (variance, standard deviation) as intermediary elements between historical service time points and future predictions. These intermediaries serve as bridges that transform raw historical data into meaningful prediction inputs, enabling accurate predictions without directly complex modeling of all historical patterns
2Measurement precision
If correction coefficients are applied to enhance prediction accuracy, then service frequency prediction precision is improved, but computational requirements increase
Solution Approach 1:
The system applies correction coefficients that modify statistical parameters (variance, standard deviation) based on specific conditions such as time periods, locations, or service types. By changing these parameters dynamically rather than using fixed values, the system achieves higher prediction precision while keeping computational operations relatively simple through parameter adjustment rather than complex recalculation
3Reliability
If the system processes large amounts of historical service time point data, then prediction reliability is improved, but data processing time increases
Solution Approach 1:
The system extracts key statistical features (variance, standard deviation, average service time points) from large amounts of historical data, rather than processing all raw data for each prediction. By taking out only the essential statistical characteristics needed for prediction, the system maintains prediction reliability while significantly reducing data processing time for each prediction query
Data Source
AI summary
The present disclosure relates to systems and methods for determining a predicted distribution of future transportation service time point. The systems may perform the methods to obtain historical service time points of transportation service requests occurred in a predetermined region; determine a variance of the historical service time points; determine a predicted distribution of future transportation service time point in the predetermined region based on the variance; and store the predicted distribution in a database.