Route Planning Using Dynamic Time-Dependent Costs
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Solution Overview
Problem
Existing route planning systems are limited in accuracy due to reliance on static road speeds, which fail to account for congestion, especially in areas not covered by comprehensive traffic monitoring systems, leading to suboptimal route suggestions and increased travel times.
Innovation Solution
A method that combines fixed, pre-defined time-independent costs with time-dependent costs for route planning, using a map database and software to automatically adjust route costs based on real-time traffic conditions, allowing for more accurate prediction of transit times and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static, pre-defined time-independent costs are used for route planning, then geographical coverage is maintained and user convenience is preserved, but route planning accuracy deteriorates due to inability to account for congestion
Solution Approach 1:
The patent applies dynamics by transitioning from static, pre-defined road costs to dynamic, time-dependent costs that automatically adjust based on real-time traffic conditions. The system continuously updates road segment costs reflecting current congestion levels, travel times, and traffic patterns, enabling the route planning algorithm to adapt to changing conditions without requiring manual intervention or complex infrastructure changes.
Solution Approach 2:
The patent changes the cost parameter from fixed, pre-defined values to time-dependent variables that reflect actual traffic conditions. By transforming the cost parameter into a dynamic value that varies with time and traffic状况, the system achieves more accurate route planning while maintaining compatibility with existing navigation infrastructure and map databases.
2Loss of time
If time-dependent traffic data is integrated into route planning, then travel time prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent implements self-service by enabling the navigation system to automatically collect, process, and apply time-dependent traffic data without requiring external intervention. The system autonomously monitors traffic conditions, updates road segment costs in real-time, and recalculates optimal routes, reducing the need for manual data entry or complex external processing infrastructure.
Solution Approach 2:
The patent applies feedback by continuously monitoring actual traffic conditions and using this information to update road segment costs and recalculate routes. The system creates a closed-loop feedback mechanism where real-time traffic data informs cost adjustments, which in turn guide route selections, and subsequent traffic data validates and refines the model further.
3Measurement precision
If comprehensive traffic monitoring infrastructure is deployed, then congestion detection accuracy is improved, but infrastructure cost increases
Solution Approach 1:
The patent uses mobile phones and GPS-equipped vehicles as intermediaries to collect traffic data without requiring dedicated monitoring infrastructure. These existing devices act as distributed sensors that report location and speed information, which the system aggregates to infer congestion conditions, eliminating the need for expensive loop detectors, cameras, or radar systems while maintaining accurate congestion detection.
Solution Approach 2:
The patent creates a virtual model of traffic conditions by collecting and processing data from mobile devices, effectively copying the function of physical monitoring infrastructure through software-based solutions. This virtual traffic monitoring system replicates the capabilities of traditional infrastructure using existing consumer electronics, significantly reducing deployment costs.
Data Source
AI summary
The present invention combines the geographical coverage possible with fixed, pre-defined route segment costs (e.g. the legal speed limit) with, wherever possible, richer time dependent costs. A user of, for example, a portable navigation device, can therefore continue route planning as before to virtually any destination in a country covered by the stored map database, but wherever possible, can also use traffic data with time-dependent costs, so that the effect of congestion with any time predictability can be accurately taken into account as an automatic, background process. It leaves the user to simply carry on driving, following the guidance offered by the navigation device, without needing to be concerned about congestion that exists now, and whether it will impact his journey.