Dynamic Route Offsets for Demand-Responsive Traffic Incentives
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Solution Overview
Problem
Transportation systems face challenges due to increasing congestion, emissions, and infrastructure deterioration, with existing incentives like congestion pricing leading to inefficient outcomes and disproportionate impacts, making it difficult to align traveler behaviors with system-wide optimization.
Innovation Solution
A method and system that determine optimal routes by considering traffic demand data and offset values, allowing users to select routes based on incentives such as credits or fees, which are dynamically adjusted to manage network flow and equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If congestion pricing and existing incentives are implemented, then system-wide optimization is pursued, but inefficient outcomes and disproportionate impacts occur
Solution Approach 1:
The patent applies local quality by differentiating route options based on their specific characteristics (optimal vs. alternative routes) and assigning different offset values to each. Instead of applying a uniform congestion pricing scheme, the system evaluates individual route properties and creates tailored incentives that account for local variations in traffic patterns, distance, and congestion levels, thereby achieving more efficient and equitable outcomes.
Solution Approach 2:
The patent implements dynamics by making offset values time-varying and demand-responsive. The system dynamically adjusts offset values based on real-time traffic demand data, allowing the incentives to adapt to changing conditions. This dynamic adjustment enables the system to respond to varying congestion levels and travel patterns, improving both efficiency and fairness compared to static pricing schemes.
2Productivity
If existing incentives like congestion pricing are applied, then travel demand is managed, but implementation costs are very high
Solution Approach 1:
The patent applies copying by using computational models and simulations to replicate and analyze traffic patterns before implementing the incentive system. The system creates virtual copies of the transportation network and uses these models to test different offset scenarios, allowing policymakers to predict outcomes and optimize the incentive structure before actual deployment, thereby reducing implementation costs and complexity.
Solution Approach 2:
The patent introduces an intermediary layer in the form of a computational system that mediates between traffic demand management objectives and implementation constraints. This intermediary uses algorithms and data processing to translate complex policy goals into actionable offset values, simplifying the implementation process and reducing the computational and administrative costs associated with deploying congestion pricing schemes.
3Productivity
If optimal incentives are derived to align traveler behaviors, then system performance improves, but deriving optimal incentives is difficult
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring actual travel behavior and traffic conditions, then using this information to adjust offset values. The system incorporates feedback loops that compare predicted outcomes from the computational models with actual observed behavior, allowing iterative refinement of the incentive structure. This feedback approach simplifies the derivation of optimal incentives by using real-world data to guide policy adjustments.
Solution Approach 2:
The patent replaces complex mechanical calculations for deriving optimal incentives with computational algorithms and data processing techniques. Instead of relying on cumbersome mathematical optimization models, the system uses programmed algorithms that automatically process traffic demand data, evaluate route characteristics, and generate appropriate offset values. This substitution of mechanical systems with computational methods significantly reduces the difficulty and complexity of deriving optimal incentives.
Data Source
AI summary
Provided is a system, method, and device for generating dynamic routes in a transportation network. The system includes a processor configured to receive a transportation request comprising an origin and a destination within a transportation network, determine a plurality of routes from the origin to the destination, wherein a first route of the plurality of routes comprises at least one optimal route among the plurality of routes, determine, for at least a subset of routes of the plurality of routes, an offset value based on traffic demand data in the transportation network, the offset value provided by a system separate and remote from the computing device, determine a selected route of the plurality of routes, allocate the offset value corresponding to the selected route to the transportation request, and modify trip data for the selected route based on the offset value.