Behavioral Changes
between the Two Time Periods inYokohama and Toyosu
Sep. 23, 2019
Tokyo University of Science Team B
Takanori Kondo, Gaku Sasaki, Yu Jinno, Yuki
Terazono, Kotaro Yamada(JR East)

1.1Research Context(背景）■Innovation in Work Style (働き方改革)• Jisa-Biz (時差Biz)- Began in 2017 by the Japanese government- Encourages the shift in the departure time of commuting trips toalleviate the congestion in the morning peak period- Encourages tele-working as well

• Have perhaps affected workers’ behavior in recent years■

Other Changes in work style• Increase in women's participation rate
• Departure from prolonged work
• Promoting elders’ participation

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1.2Actual Changes（実際の変化）

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1.3Research（研究方針）
■

■

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Objective• To identify changes in behavioral pattern between two
different time periods, especially considering possible
changes in work style• To model travel-activity patterns from two different datasets
Data• Two trip data sets from probe-person surveys in Yokohama
in 2008 and Toyosu in 2018

•Methodology• Tour-based analysis focusing on tour patterns- Tour = Home-based trip chain

1.4Aggregation Results（基礎集計結果）

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1.5Aggregation Results（基礎集計結果）

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2.1Modeling （モデル推定）
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■

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We use multiple regression model• We would like to compare the estimation result of two
data sets(Toyosu and Yokohama)𝑦"#$_&'(# = 𝛽+𝑥+ + 𝛽.𝑥. + 𝛽/𝑥/ + ・・・𝛽.0𝑥.0
𝑦122_&'(# = 𝛼+𝑥+ + 𝛼.𝑥. + 𝛼/𝑥/ + ・・・𝛼.0𝑥.0

出発時間と到着時間を目的変数に,性別,,年齢層,行動パターン,トリップ数,距離,トリップ時間をダミー変数を交えてモデル作成を試みる
パラメータを比較することで,時代変化の影響を捉える■2.2 Model estimation result（モデル推定結果）■

・推定結果の表

出発時間8/11

Spare slide

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Data Cleaning Procedure

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1. Remove trips without OD location redord.
2. Combine two consecutive trips with the same purpose andless than one minute in between.
3. Order trips of each user in departure time and define a trip
chain ending a return-to-home trip as a tour.
4. Remove all trips belonging to a tour whose origin anddestination are at different locations.
5. Remove all trips belonging to a tour any two consecutive
trips of which are unconnected (destination and origin are
distant more than 400m).
6. Remove trips which consists an entire tour (i.e one-triptour).
7. Remove trips belonging to a more than 24-hour-long tour.

Result of Data Cleaning - Number of Valid Records -11/11■●Toyosu, 2018
● Trips: 4,733 / 17,600 (27% of the original)● Tours: 1,872● Participants: 136 / 304 (45% of the original)
Yokohama, 2008
● Trips: 3,070 / 13,808 (22% of the original)● Tours: 1,219● Participants: 62 / 133 (47% of the original)Attributes of Participants■■

Yokohama, 2008

Toyosu, 2018

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Aggregation ResultsYokohama, 2008
Trip distance

Trip time

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Toyosu, 2018

How to Define Tour Pattern

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1. Classify trips by their purpose into three categories:• H: Return to home
• W: Commute or return to the office or school or business• O: Other
2. Represent a tour pattern as a string of the purpose classification• Ex. Working - Shopping - Return-to-home -> HWOH
3. Classify the tour pattern into six categories:• HWH: starts with HW ends with WH• HOH: has only Os between H…H• HWOH: starts with HW and ends with OH.
• HOWH: starts with HO and ends with WH.
• HOWOH: starts with HO, has at least one W and ends with OH.

Aggregation Results

Yokohama, 2008

Toyosu, 201815/11