Master thesis / Ph.D. Dissertation

Proposal and Evaluation of Bus Arrival Time Prediction Method
by Convolution of Operation and Weather Information

Public transportation has become an indispensable part of modern life, and is used for various purposes such as commuting to work and school. Past research has shown that the convenience of public transportation has a direct impact on the willingness of users to use it. One of the ways to improve the convenience of bus routes is to provide arrival time prediction to users. However, it is very difficult to predict the arrival time accurately, especially in the case of bus routes, because of the complexity of the factors involved. In recent years, LSTM has been attracting attention for its ability to handle the characteristics of long time series, which cannot be handled by methods such as linear regression, in predicting the arrival time of bus routes.


As an extension of the LSTM-based method, a Convolutional LSTM-based method using convolutional operations for weight operations has been proposed. However, there are two major problems with this method. The first is that it does not take into account features unique to timetable-based methods, such as intentional stops at bus stops due to early arrivals, because it evaluates routes that run at regular intervals without a timetable, and the second is that its prediction accuracy drops significantly when the operation is disrupted by weather conditions such as rain. In order to solve these problems, we propose a method that simultaneously collapses weather data and past operation data using Convolutional LSTM, and uses separate models to predict travel time and stop time.

We compared the prediction accuracy of our method with that of an existing study using Convolutional LSTM for a one-week period on a bus route in Kobe City, Hyogo Prefecture, and found that the mean absolute percentage error decreased by 1.36% for the entire week. In particular, in the evening hours and during rainy weather, the mean absolute percentage error was significantly reduced by about 3% compared to the entire week.


  • 石長 篤人, 新井 イスマイル, 垣内 正年 and 藤川 和利, "運行情報と気象情報の畳み込みによるバス到着時刻予測手法," 研究報告高度交通システムとスマートコミュニティ(ITS), 情報処理学会, vol.2021-ITS-84, no.6, pp1 -8, 2021年3月4日