Traffic Density Prediction Using Trajectories and Entering Particles
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Solution Overview
Problem
Existing traffic density prediction methods require laborious tuning and calibration, and there is a need for a more efficient system and method to estimate future traffic density in environments, particularly in indoor and outdoor spaces, which is crucial for congestion prediction and route planning.
Innovation Solution
A traffic density estimation model using a trajectory prediction model, an entering particle prediction model, and an iterative sampling model, based on a transformer architecture, to predict the movements of objects and account for entering and exiting particles, enabling accurate estimation of future traffic density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-driven methods are used for traffic density prediction, then prediction capability is achieved, but laborious tuning and calibration is required and significant human involvement is needed
Solution Approach 1:
The system uses unsupervised learning to automatically discover traffic patterns and predict density without requiring manual tuning or calibration. The model self-adjusts to learn from historical trajectory data, eliminating the need for human experts to configure network topology and system logic.
Solution Approach 2:
The patent replaces the mechanical model-driven approach (which requires manual setup and tuning) with a data-driven machine learning approach. The system substitutes complex manual configuration processes with automated algorithms that learn traffic patterns directly from data.
2Measurement precision
If accurate traffic density estimation is achieved, then optimal deployment of service robots and traffic control is enabled, but complex prediction models are required
Solution Approach 1:
The system extracts only the essential features needed for prediction - historical trajectory data of objects in the environment. By focusing on relevant movement patterns rather than attempting to model all environmental factors, the system achieves accurate predictions with a simpler approach.
Solution Approach 2:
The patent changes the approach from modeling complex environmental parameters and network topology to directly analyzing trajectory data parameters. The system transforms the problem from modeling traffic flow mechanics to statistical pattern recognition in movement data.
Data Source
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AI summary
The present disclosure provides a system and a method for estimating a future traffic density in an environment. The method comprises receiving, for at least one object in the environment, at least one partial trajectory and a sequence of observation vectors. The at least one object is represented by a plurality of particles. The method comprises processing the at least one partial trajectory with a trajectory prediction model to predict a location of each particle of the plurality of particles at a future time instant and processing the sequence of observation vectors with an entering particle prediction model to predict a probability of observing an entering particle at each ingress point at the future time instant. The future traffic density is estimated based on the predicted location of each particle and the predicted probability of observing the entering particle at each ingress point at the future time instant.