京都大学教育研究振興財団助成事業
成 果 報 告 書
4 年 4 月 25 日
公益財団法人京都大学教育研究振興財団会 長藤洋 作

様所 属 部 局 工学研究科 都市社会工学専攻

助 成 の 種 類
申請時の科研費
研 究 課 題 名

職

名

准教授

氏

名

Jan-Dirk Schmöcker

令和 3 年度 ・研究活動推進助成

Improving Public Transport Network Planning with Map and
Google Popular Times data

上記以外で助成金を 充 当 し た
研 究 内 容（所属・職名・氏名）

助成金充当に関わる共同研究者

発表学会文献等

Wenzhe Sun, 京都大学 工学研究科 非常勤研究員（この研究成果を発表した学会・文献等）
Vongvanich, T., Schmöcker, J.-D., Sun, W., Yamada, T. and Nakao, S. (2022). Explaining station
demand patterns using Google Popular Times data. 4th International KEC Conference, Kantipur
Engineering College, Nepal, 28 April, online.
Sun, W. and Schmöcker, J.-D. (2022). Learning the time-varying relationship between transit
stop flows and nearby POI busyness. 15th International Conference on Advanced Systems in
Public Transport (CASPT). 3-7 October, Tel Aviv, Israel.

成 果 の 概 要

研究内容・研究成果・今後の見通しなどについて、 簡略に、A４版・和文で作成し、添付して下さい。（タイトルは「成果の概要／報告者名」）
交付 を 受 けた 助成 金額

1,000,000円

使 用 し た 助 成 金 額

1,000,000円

返納すべき助成金額0円

費会計報

告助成金の使途内訳目金額

モバイル空間統計アカデミックパック

957,000

非常勤職員給与・社会保険料

26,056

オンライン学会参加費

16,100

消耗品

844（今回の助成に対する感想、今後の助成に望むこと等お書き下さい。助成事業の参考にさせていただきます。）

当財団の助成についてこの基金は、今年も研究を続けるために大変役に立ちました。学会への論文投稿の準備や、さらなる研究資金獲得のためにも役立ちました。特に、さまざまな研究の方向性を探ることができる基金の柔軟性に感謝しています。

成果の概要 : “Improving Public Transport Network Planning with Map and Google PopularTimes data”／Jan-Dirk Schmöcker
Google Popular Times (GTP) data are a novel data
source that is open to the public, accessible in realtime and available in areas around the world. Thedata are available from webpages for “points ofinterests” (POIs) which can be a wide range ofpublic facilities. Below figure shows a generalexample of the information (red bar is liveinformation). We have been collecting theinformation from a wide number of POIs in Kyoto for the whole year. Static information (blue bars) could
be obtained from 10121 POIs and both static and live information from 1524 POIs.
The conducted research aimed to explain and predict travel demand patterns for train stations in Kyoto citywith this data. In particular, we wanted to understand if it is possible to explain why people travel to astation, by comparing the time series of station GPT data and those of nearby attraction POIs such as shops,restaurants and tourist attractions. Multiple linear regression models are developed using the POI data toanalyse the correlation. The first set of our regression models aim to identify POIs and POI types that havethe highest impact on the demand at each station. The subsequent models use station live popularity, station
historical popularity and POI visitation to predict demand.
The research showed that we are able to identify influential POIs and quantify their impacts given that there
is a sufficient number of POIs in the vicinity of the station. The figure shows one example of the results
where time indicates the time lag of prediction. For
prediction, the station’s historical popularity and live
popularity are most significant when compared to its
surrounding POIs. POI visitation data are shown to be
useful when trying to predict further into the future.
Our findings suggest that GPT data can enable transit
planners and transit users to predict station demand inreal-time. City planners would also gain valuable
insights in understanding for what type of activities
travellers alight or board from each station.
Moreover, the method can be scaled and applied toother types of transit stations in other cities.
In addition to this research we have also been collecting and using GPT data from Shizuoka to understandorigin destination patterns in passenger flow. Here also the daily patterns in busyness of specific locations
can explain and predict the bus demand. We are using for this regression analysis as well as advanced
machine learning methods.
In future research we aim to continue these research directions. For example, there is still a lot to explore
based on the initial Kyoto data analysis and to analyse, for example, time of day effects. Furthermore, weaim to fuse the GPT data with other data from Kyoto. Part of the obtained funding we have used to purchasemobile spatial statistics of Kyoto that provide us information as to the distribution of Kyoto’s population
within the city. We suggest that together with the GPT data we can obtain “an activity origin-destination
matrix. We are further considering specific transport planning applications, such as exploring the tourpatterns of tourists using the GPT data and then estimating the demand for new transport schemes such as
shared bicycles and scooters. This has been the topic of a new Kaken proposal.