Dynamic Traffic Route Simulation With Behavior Feedback
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Solution Overview
Problem
Existing traffic simulation methods fail to accurately simulate the dynamic states of transportation modes, leading to inaccuracies in predicting travel routes and their impacts on city planning and transportation management.
Innovation Solution
A computer program that searches for multiple travel routes using different transportation modes, predicts route selection based on behavior models, simulates movements along a time axis, and updates features to reflect changing congestion and other dynamic states, thereby improving simulation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic simulation methods are used, then the simulation process is simple, but the accuracy of predicting travel routes and transportation dynamics is insufficient
Solution Approach 1:
The patent implements dynamic simulation by continuously updating transportation mode states (congestion levels, availability) and route selections over time. The behavior model dynamically adjusts predictions based on current system state, and the simulation progresses through discrete time steps where features are updated based on movement results, creating a dynamic rather than static simulation process
Solution Approach 2:
The patent incorporates feedback mechanisms where the simulation results (movement outcomes) are used to update transportation mode features, which then feed back into the behavior model for subsequent route predictions. This closed-loop feedback system allows the simulation to adapt and improve accuracy by learning from its own predictions and outcomes
2Reliability
If static route prediction is used, then the prediction process is fast, but it cannot capture the interdependencies between route selections and transportation mode dynamics
Solution Approach 1:
The patent performs preliminary actions by pre-defining multiple candidate travel routes and their associated transportation modes before the actual simulation runs. The behavior model is pre-configured with features indicating transportation mode states, allowing the system to quickly select from pre-prepared options while still capturing dynamic interdependencies during simulation execution
Solution Approach 2:
The system transitions from static to dynamic prediction by continuously updating transportation mode features based on simulated movements and feeding these updates back into the behavior model. This allows the prediction to adapt to changing conditions while maintaining computational efficiency through structured simulation steps
Data Source
AI summary
A computer searches for a plurality of travel routes along which a person moves from a departure point to a destination using one or more transportation modes among a plurality of transportation modes, the one or more transportation modes being different from each other among the plurality of travel routes, predicts a first travel route to be selected by the person from the plurality of travel routes by using a behavior model for predicting a selection behavior based on features indicating states of the plurality of transportation modes, simulates a first movement of the person along a time axis using the first travel route, and updates the features using a result of the first movement.