JSAI2023

Presentation information

General Session

General Session » GS-10 AI application

[1M3-GS-10] AI application

Tue. Jun 6, 2023 1:00 PM - 2:40 PM Room M (D1)

座長:秋山 祐樹(東京都市大学) [オンライン]

1:40 PM - 2:00 PM

[1M3-GS-10-03] A spatio-temporal pattern extension method for predicting traffic jams deviating from past traffic patterns

〇Kengo Okano1, Takahiro Suzuki1, Ryoma Nakamura1, Masaki Matsudaira2 (1. Oki Electric Industry Co., Ltd., 2. Oki Consulting Solutions Co., Ltd.)

Keywords:probe data, traffic flow prediction

We have researched a traffic flow prediction method using probe data to provide accurate, real-time traffic information for the purpose of reducing traffic jams and accidents. The percentile method, which statistically predicts traffic flow several hours in advance, has a problem with accuracy because it cannot predict traffic jams at certain times of the day or traffic jams extending beyond a certain length that deviated from the learned pattern. Therefore, we developed a time dilation method and a space-time dilation method for training data in response to changes in traffic density, and applied them to this method. As a result, we confirmed that it is possible to predict traffic jams and extended traffic jam lengths at times that have not been learned in the past, and achieved improved accuracy.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.

Password