Congestion Prediction Models for Sensor-Sparse Areas
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Solution Overview
Problem
Conventional congestion prediction systems only provide current congestion levels, failing to predict future congestion, which limits users' ability to avoid congestion, thereby reducing satisfaction for future users.
Innovation Solution
A learning device and method that utilizes past congestion-area data and sensor data to generate models for predicting future congestion levels in areas without sensors, using a first model to predict current congestion levels and a second model to forecast future congestion based on past and current data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed in all areas to be estimated, then measurement precision of congestion levels is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the sensor network by training a deep neural network model to replicate the functionality of physical sensors. The model learns from sensor data collected in areas with sensors and generates congestion level predictions for areas without sensors, effectively copying the measurement capability without physical deployment
Solution Approach 2:
The patent introduces timetable information as an intermediary data source that bridges the gap between areas with sensors and areas without sensors. This intermediary information helps the model infer congestion levels in unsensorized areas by considering the dynamic causal relationship between train schedules and passenger flow patterns
2Productivity
If only current congestion levels are predicted, then productivity of prediction system is maintained, but loss of information about future congestion occurs
Solution Approach 1:
The patent performs preliminary prediction of future congestion levels before users make travel decisions. The system forecasts congestion at multiple future time points (e.g., 5 minutes, 10 minutes, 15 minutes ahead) using the trained deep neural network, enabling users to take preventive action before encountering congestion
Solution Approach 2:
The patent implements dynamic prediction by continuously updating congestion forecasts as new sensor data arrives and time progresses. The system adapts to changing conditions by retraining the model with latest data and generating updated predictions, maintaining accuracy while providing forward-looking information
Data Source
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AI summary
A congestion prediction device (120) includes a correction-model generating unit (152) that generates a correction model that is a learning model for predicting, from values detected by one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection, where the first model is generated by using values indicated by past sensor data indicating the values detected in the past by the one or more sensors installed in n areas out of m areas (where m is an integer of two or more) as input data and using the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas as correct data; and a model learning unit (162) that generates a prediction model that is a learning model for predicting a future congestion level from the congestion-related information, by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second time point being a time point after the first time point.