18474320. CONGESTION PREDICTION DEVICE, CONGESTION PREDICTION METHOD, AND STORAGE MEDIUM simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

CONGESTION PREDICTION DEVICE, CONGESTION PREDICTION METHOD, AND STORAGE MEDIUM

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Ayumu Shiomi of Toyota-shi (JP)

Atsushi Miyagawa of Toyota-shi (JP)

Hiroki Takeuchi of Toyota-shi (JP)

Toshiyuki Kawai of Kariya-shi (JP)

Ryosuke Fukaya of Toyota-shi (JP)

Hiroyuki Tokita of Toyota-shi (JP)

Yusuke Ikenoue of Takahama-shi (JP)

Tsukasa Kato of Toki-shi (JP)

Katsuhisa Yoshikawa of Miyoshi-shi (JP)

Yuta Ichijyo of Toyota-shi (JP)

Takayuki Akaishi of Toyota-shi (JP)

Shintaro Kawai of Toyota-shi (JP)

CONGESTION PREDICTION DEVICE, CONGESTION PREDICTION METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 18474320 titled 'CONGESTION PREDICTION DEVICE, CONGESTION PREDICTION METHOD, AND STORAGE MEDIUM

Simplified Explanation

The patent application describes a congestion prediction device and method that uses machine learning to predict the degree of congestion in a specific area based on input variables related to congestion levels at regular time intervals.

• The mapping in the device outputs a variable indicating the degree of congestion in a specific area when input variables related to congestion levels are provided.
• The input variables include congestion variables that represent congestion levels in the specific area at regular time intervals within a specific period.
• The output variable predicts the degree of congestion after a lapse of the regular time interval from the end time of the specific period.

Potential Applications

This technology can be applied in traffic management systems, urban planning, and logistics to predict congestion levels in specific areas and optimize routes and schedules accordingly.

Problems Solved

This technology helps in proactively managing traffic congestion, reducing travel time, and improving overall transportation efficiency.

Benefits

The congestion prediction device can help in reducing fuel consumption, minimizing emissions, and enhancing the overall commuter experience by providing real-time congestion information.

Potential Commercial Applications

• "Traffic Congestion Prediction Technology for Smart Cities": Optimizing traffic flow in urban areas.
• "Logistics Route Optimization with Congestion Prediction": Improving delivery efficiency by avoiding congested routes.

Possible Prior Art

One possible prior art could be traditional traffic prediction models based on historical data and traffic patterns.

How does the machine learning algorithm adapt to changing traffic patterns over time?

The machine learning algorithm likely needs to be continuously updated with new data to adapt to changing traffic patterns and ensure accurate congestion predictions.

What measures are in place to ensure the privacy and security of the data collected for congestion prediction?

It is essential to address concerns regarding the privacy and security of the data collected for congestion prediction to prevent any misuse or unauthorized access.

Original Abstract Submitted

A congestion prediction device, a congestion prediction method, and a storage medium are provided. A mapping outputs an output variable when input variables are input to the mapping. The output variable indicates a degree of congestion in a predetermined specific area. The mapping is learned in advance by machine learning. The input variables include congestion variables. Each of the congestion variables indicates a degree of congestion in the specific area at regular time intervals within a specific period. The output variable indicates a degree of congestion after a lapse of the regular time interval from an end time of the specific period.