User-Centric Traffic Estimation Error Parameter

Solution Overview

Problem

Existing navigation systems face challenges in accurately updating road traffic conditions on user devices due to network issues, leading to disparities between server-estimated and user-experienced conditions, which can result in user dissatisfaction and inaccurate navigation information.

Innovation Solution

A method and system for determining a user-centric traffic estimation error parameter by calculating the difference between expected and actual travel times on the user device, allowing for real-time adjustment of traffic prediction algorithms to improve accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the server updates road traffic conditions frequently, then the accuracy of traffic predictions is improved, but network issues may cause updates to be lost on user devices, increasing user dissatisfaction

Engineering Contradiction:

Improvetraffic prediction accuracyVSAvoidupdate delivery reliability

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The system implements feedback by collecting actual travel time data from users and comparing it with predicted travel times. This feedback loop allows the server to identify discrepancies between predicted and actual conditions, enabling it to adjust its traffic prediction algorithms to account for cases where updates were not properly delivered to user devices.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables self-service by allowing user devices to independently measure and report their actual travel experiences. Users effectively serve themselves by providing ground truth data about actual traffic conditions, which the server then uses to improve its prediction models without requiring direct intervention or confirmation from the server about whether updates were received.

Inventive Principle:

Principle #25Self-service

2Device complexity

If the server relies on server-side history for adjustments, then the system complexity is reduced, but user dissatisfaction is not captured, worsening prediction accuracy

Engineering Contradiction:

Improvesystem complexityVSAvoidprediction accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system implements feedback by collecting actual travel time data from users and comparing it with predicted travel times. This feedback loop allows the server to identify discrepancies between predicted and actual conditions, enabling it to adjust its traffic prediction algorithms to account for cases where updates were not properly delivered to user devices.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system adds another dimension to the data collection by incorporating user-side measurements of actual travel time. This complements the server-side prediction data, creating a more comprehensive dataset that includes both predicted and actual conditions from different perspectives, thereby improving prediction accuracy without significantly increasing system complexity.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Loss of energy

If navigational information is not updated on user devices, then network bandwidth is conserved, but user awareness of current traffic conditions is lost, increasing travel time

Engineering Contradiction:

Improvenetwork bandwidth consumptionVSAvoiduser travel time

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system enables self-service by allowing user devices to independently measure and report their actual travel time data. Users effectively serve themselves by providing ground truth information about actual traffic conditions they experienced, eliminating the need for continuous server push updates while still capturing valuable information for improving predictions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback by collecting actual travel time data from users and comparing it with predicted travel times. This feedback loop allows the server to identify discrepancies between predicted and actual conditions, enabling it to adjust its traffic prediction algorithms to account for cases where updates were not properly delivered to user devices.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11288956B2Methods of determining user-centric traffic estimation error parameter associated with estimated road traffic conditions

Publication Date: 2022.03.29 Y E HUB ARMENIA LLC

AI summary

A method of determining a user-centric traffic estimation error parameter associated with an estimated road traffic condition that is electronically provided to a user of a device. the method comprises: at a first moment in time, acquiring an estimated travel time for a road segment, the estimated travel time having been computed for the first moment in time; responsive to the device approaching the road segment, displaying an application-generated estimated travel time for the road segment, the application-generated estimated travel time being based on a most recently acquired estimated travel time from the server; responsive to the device departing from the road segment, determining an actual travel time for the road segment; computing the user-centric traffic estimation error parameter based on the application-generated estimated travel time and the actual travel time; and transmitting the user-centric traffic estimation error parameter to the server for adjusting a traffic prediction algorithm.