Transport Usage Data Distribution via Subset Sampling
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Solution Overview
Problem
Existing methods struggle to accurately distribute revenue among different transport operators within a fare network based on unequal usage of various transport modes by users, lacking spatial and temporal resolution in determining individual transport usage.
Innovation Solution
A method involving sensor-based data collection from a subset of individuals, combined with stochastic extrapolation, to determine mode-specific route usage, which is then extrapolated to the entire population, using mobile devices and a central processing unit to analyze and distribute revenue fairly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based data collection is performed on all individuals continuously, then measurement precision and reliability of transport usage data are improved, but device complexity, data processing requirements, and privacy concerns increase
Solution Approach 1:
The patent divides the population into a subset of individuals (e.g., 1-10% of the total population) who are selected to participate in the sensor-based data collection. This segmentation allows the system to collect detailed, precise data from a manageable group while avoiding the complexity of monitoring all individuals continuously. The subset is statistically representative and enables reliable extrapolation to the entire population.
Solution Approach 2:
Instead of collecting data from all individuals (excessive action), the patent applies partial action by selecting only a subset of individuals for sensor-based monitoring. This partial data collection is sufficient to achieve statistically valid results when combined with appropriate sampling methods and extrapolation techniques, thereby reducing system complexity while maintaining measurement precision.
2Reliability
If mode-specific route usage is determined for all individuals, then reliability of revenue distribution is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent segments the population into a subset of individuals for whom mode-specific route usage is actually measured using sensors. By limiting the direct measurement to this subset (e.g., 1-10% of the population), the system reduces processing time and computational resources while maintaining reliability through statistical extrapolation to the entire population.
Solution Approach 2:
The patent creates a statistical copy or representation of the entire population's transport behavior by measuring only a subset of individuals. The data from this subset is used to extrapolate and infer the transport usage patterns of the entire population, thereby reducing the time and resources needed for direct measurement while maintaining reliable estimates for revenue distribution.
3Measurement precision
If detailed spatial and temporal data is collected on all individuals, then measurement precision is improved, but loss of information related to privacy increases
Solution Approach 1:
The patent segments the population into a subset of individuals who are monitored with detailed sensor data. By limiting detailed spatial and temporal data collection to this subset rather than all individuals, the system maintains high measurement precision for the monitored group while minimizing the exposure of sensitive privacy information across the entire population.
Solution Approach 2:
The patent extracts or removes personally identifiable information and detailed privacy-sensitive data from the dataset after analysis. By taking out and removing this sensitive information, the system preserves the measurement precision needed for accurate revenue distribution while protecting individual privacy and reducing the loss of information related to personal identity.
Data Source
Figure 1
AI summary
The invention relates to a method for determining the distribution of the spatially resolved use of means of transport by a group of people consisting of a large number of individuals during a study period, comprising the following steps: - Repeatedly and distributed over the study period, automated central querying of the sensor-based, means-of-transport route usage for a specific sub-study period of individuals from a specific sub-set of people, wherein the sub-set of people is formed as a subset of randomly selected individuals from the group of people and which sub-study period is a fraction of the study period, - Storing the centrally queried, means-of-transport route usage, - Extrapolating the probable means-of-transport route usage of the group of people based on the stored means-of-transport route usage for the entire study period;- Output of a transport-mode and route-specific unit according to the extrapolated passenger-related use of the transport vehicles.;