Dynamic Travel Transaction Sensing Device
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Solution Overview
Problem
Conventional fixed fee systems for services like public transportation fail to accurately represent the value received by customers due to factors such as congestion, travel time, and passenger load, leading to overcharging or undercharging.
Innovation Solution
Implementing dynamic travel transactions that utilize real-time data from sensing devices, including user device data and travel variability data from sources like traffic cameras, RFID scanners, and GPS, to adjust fees based on actual service conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed fee systems are used for transportation services, then service providers can simplify pricing and collection processes, but customers are overcharged or undercharged because fees do not reflect actual service quality and conditions
Solution Approach 1:
The patent implements dynamic pricing by continuously adjusting transportation fees based on real-time sensor data about service conditions (congestion, travel time, route changes). This transforms the static fixed-fee model into a dynamic system that automatically adapts to changing conditions, resolving the contradiction between measurement precision and system complexity through automated real-time adjustments.
Solution Approach 2:
The system establishes a feedback loop where sensor data about actual service delivery (traffic conditions, passenger load, route deviations) is continuously collected and used to adjust pricing. This feedback mechanism ensures fees accurately reflect service quality without requiring complex manual intervention, as the system self-regulates based on measured conditions.
2Measurement precision
If real-time sensor data is collected and processed to determine dynamic travel transactions, then fee accuracy is improved, but computational strain and memory burdens increase
Solution Approach 1:
The patent pre-processes and stores sensor data in structured formats during data collection phases, organizing information about traffic conditions, route parameters, and service metrics before transaction processing occurs. This preliminary organization reduces the computational burden during actual transaction determination, as data is already filtered and structured for efficient analysis.
Solution Approach 2:
The system divides the complex task of transaction determination into separate functional modules: data collection sensors, data processing units, and transaction calculation engines. Each component handles specific aspects of the process independently, reducing overall computational strain by distributing processing loads across multiple specialized units rather than requiring one complex processing system.
Data Source
AI summary
Sensing devices and associated methods are disclosed for providing dynamic travel transactions. An example sensing device includes a sensor associated with a vehicle and a controller operably coupled with the sensor. The sensor is configured to generate first device data of a first user device associated with a first user where the first device data includes first trip data of the vehicle and the first user. The sensor is further configured to generate travel variability data associated with the vehicle where the travel variability data includes one or more vehicle operating parameters that vary during travel of the vehicle. The controller of the sensing device is configured to generate a travel transaction based upon the first device data of the first user device and the travel variability data associated with the vehicle.