新型コロナウイルスの流行に伴う時間価値の変容
Change in the Value of Travel Time due toPandemic of the COVID-19
3 東京大福田研/Univ. of Tokyo Fukuda Lab.
荒井・田端・長谷川・江橋
Arai, Tabata, Hasegawa, Ebashi

Background
新型コロナウイルスの流行で人々の生活様式が変化した．
The new coronavirus epidemic has changed people's lifestyles.

・テレワークが普及した．
・混雑への抵抗感が増した．

Telework has become popular.
Resistance to congestion has increased.

人々の時間価値も変化し，求められる都市の姿が変容したのではないだろうか．
As the value of people's time has changed, the shape of the city that people want has changed.

目的 Objective
豊洲周辺の人々の時間価値の変化を2019年と2020年で比較し，今後の都市計画について検討する．
Changes in the time value of people in the Toyosu area will be compared between 2019 and 2020, andfuture urban planning will be discussed.

2Hypothesis

3

1.時間価値が低下している Time value of money is declining.
時間がかかっても徒歩や自転車を選択しているのではないだろうか．
They may choose to walk or bike even if it takes longer.

monetary value of congestion
2.混雑不効用の金銭価値は増加している The
disutility is increasing.
混雑を避けた時間帯に公共交通機関を利用しているのではないだろうか．
They may be using public transportation at times when they can avoidcongestion.

Data4▼使用したデータUsed data
豊洲PPデータ（2019年/2020年の10月~12月）

Toyosu-PPdata between October to December in 2019/2020▼データクリーニング Data cleaning
OD直線距離500m 以上 OD Direct Distance >500m▼目的の分類 Categorize objectives as follows,
業務 ：目的が「通勤・通学」、「帰社・帰校」、「業務」
Business："Business", "Commute to work/school", "Return to work/school"
非業務：目的が上記以外
Non-business：Other purposes▼ピーク時間ダミー Peak hour dummy
1：午前7~9時 or 午後5~7時 1： 7~9 a.m. or 5~7 p.m.
0：Times other than the above
0：上記以外の時間帯

Basic Analysis
非業務Non-business

Ratio(%)

業務 Business

5Train・SubwayBusWalkBileCarOthers

・公共交通機関（鉄道地下鉄，バス）の割合が減少している．
The ratio of public transportation (railroad, subway, bus) is decreasing.

・私有交通機関（徒歩，自転車，乗用車）の割合が増加している．
The percentage of private transportation (walking, bicycling, and passenger cars) is increasing.

Model for Estimation
多項ロジットモデル (Multinominal Logit Model)
𝑉𝑤𝑎𝑙𝑘 =

6+𝛽6 𝑡𝑤,𝑛𝑉𝑐𝑎𝑟 = 𝛽1 + 𝛽7 𝑡𝑐,𝑛𝑉𝑏𝑖𝑘𝑒 = 𝛽2 + 𝛽8 𝑡𝑖,𝑛𝑉𝑏𝑢𝑠 = 𝛽3 + 𝛽9 𝑡𝑏,𝑛 + 𝛽11 𝑐𝑏,𝑛 + 𝛽12 𝑑𝑏,𝑛walk bikecarbus

train

𝑉𝑡𝑟𝑎𝑖𝑛 = 𝛽4 + 𝛽10 𝑡𝑡,𝑛 + 𝛽11 𝑐𝑡,𝑛 + 𝛽13 𝑑𝑡,𝑛

多項ロジットモデルの所要時間パラメータやピーク時間ダミーをそれぞれ別のものに設定
Parameters of travel time and peak hour dummy were set for each mode.

しかし、However

費用のパラメータが正となる
parameter of cost was positive.

今回は、パラメータが共通のモデルとする
We set the same parameter for travel time and peak hour dummy.

Model for Estimation
多項ロジットモデル (Multinominal Logit Model)
𝑉𝑤𝑎𝑙𝑘 = + 𝛽5 𝑡𝑤,𝑛𝑉𝑐𝑎𝑟 = 𝛽1 + 𝛽5 𝑡𝑐,𝑛𝑉𝑏𝑖𝑘𝑒 = 𝛽2 + 𝛽5 𝑡𝑖,𝑛𝑉𝑏𝑢𝑠 = 𝛽3 + 𝛽5 𝑡𝑏,𝑛 + 𝛽6 𝑐𝑏,𝑛 + 𝛽7 𝑑𝑏,𝑛walk bikecarbus

train

時間価値 (Value of travel time)
Coefficient for travel time of each mode各交通手段の所要時間の係数(𝛽5 )𝑉𝑇 =費用の係数(𝛽6 )Coefficient for cost

𝑉𝑡𝑟𝑎𝑖𝑛 = 𝛽4 + 𝛽5 𝑡𝑡,𝑛 + 𝛽6 𝑐𝑡,𝑛 + 𝛽7 𝑑𝑡,𝑛𝑐𝑘,𝑛 : cost for travel mode 𝑘 and individual 𝑛𝑡𝑘,𝑛 : travel time for travel mode 𝑘 and individual 𝑛𝑑𝑏,𝑛 : dummy variable for crowdedness for mode 𝑘and individual 𝑛

混雑の金銭価値 (Value of crowdedness)
Coefficient for dummy variable for peak hour
ピーク時間ダミーの係数(𝛽7 )𝑉𝐶 =費用の係数(𝛽6 )Coefficient for cost7

Simple Analysis

2019年と2020年のデータについて分析 （2パターン）
Analysis of 2019 and 2020 data (2 patterns)8Model Estimation Table
Including peak hour (dummy)
2019

9Not including peak hour (dummy)

2020

2019

2020

Independent Variable

Coef.

t-valueCoef.

t-valueCoef.

t-valueCoef.

t-value

Travel time [min/100]
Fare [yen/100]-187.80-0.27-29.06***-16.32***-236.68-0.22-35.44-14.55-190.53-0.29-29.45***-17.19***-230.01-0.22-35.81***-14.54***

Peak hour [1/0]0.82

12.19***

0.58

10.16

----Constant（train）

0.94

12.76***

0.31

4.97

1.27

18.44***

0.50

8.57***

Constant（bus）

0.34

4.55***-1.23-17.220.72

10.55***-1.09-15.05***Constant（car）-1.64-35.54***-1.77-42.96-1.65-35.78***-1.78-43.22***

Constant（bike）-16.23-0.49-21.85-0.11-15.99-0.60-18.00-0.64

NLog likelihood (Initial)
Log likelihood (Final)

10,840-13330-6407

14,423-17155-7926

10,840-13330-6485

14,423-17155-7980

Rho squared

0.519

0.538

0.513

0.535

Adjusted rho squared

0.519

0.538

0.513

0.535
*p<0.10**p<0.05***p<0.01

Change in Value of Time/Crowdedness
ピーク時間ダミーなし
Including peak hour (dummy)

時間価値 (円/min)Value of time

2019

2020

11.12

18.43

ピーク時間ダミーあり
Not including peak hour (dummy)
2019

2020

時間価値 (円/min)Value of time

11.4

18.1

混雑の金銭価値 (円/ピーク時間ダミー) Value of crowdedness

-3.00

-2.68

1.時間価値が上昇

Value of travel time increases.

→仮説と異なる結果

Different from hypothesis.

2.混雑の金銭価値が上昇

Value of crowdedness increases.

→仮説が検証された

Hypothesis is verified.

10

Detailed Analysis

2019年と2020年のデータについて、目的別、休日・平日別で分析（2×2×2の8パターン）
Analysis of 2019 and 2020 data by purpose, holiday and weekday (2 x 2 x 2 patterns)

112019 Estimation Table

12

Independent VariableBusiness(weekday)Coef.
t-value

Business(holiday)Coef.
t-value

Non-business(weekday)Coef.
t-value

Non-business(holiday)Coef.
t-value

Travel time [min/100]
Fare [yen/100]-213.4-0.323-17.61***-11.45***-496.6-0.347-2.28**-1.25-180.5-2.292-13.22***-8.166***-189.7-0.289-25.61***-14.65***

Peak hour [1/0]0.893

7.845***-0.311-0.2600.291

1.777*

0.748

9.743***

Constant（train）

0.956

6.942***

1.268

0.812

0.922

6.595***

1.023

11.67***

Constant（bus）

0.528

4.033***

0.861

0.629

0.070

0.409

0.465

5.347***

Constant（car）-2.120-21.81***-2.410-2.004**-0.957-11.28***-1.913-34.07***

Constant（bike）-14.300-0.468-14.80-0.037-14.18-0.667-19.90-0.068

N

3,913

179

4,859

1,889

Log likelihood (Initial)-4906-60.93-2486-10844Log likelihood (Final)-2076-18.88-1454-4825

Rho squared

0.577

0.690

0.415

0.555

Adjusted rho squared

0.575

0.575

0.412

0.554
*p<0.10**p<0.05***p<0.01

2020 Estimation Table

13

Independent Variable

Business(weekday)Coef.
t-value

Business(holiday)Coef.
t-value

Non-business(weekday)Coef.
t-value

Non-business(holiday)Coef.
t-value

Travel time [min/100]
Fare [yen/100]-167.5-0.094-15.71***-3.354***-48.200.081

1.01-0.418-274.10-0.360-20.14***-12.12***-321.7-0.418-19.16***-11.95***

Peak hour [1/0]0.915

9.063***

0.819

0.39

-0.077-0.589-0.257-1.73*

Constant（train）

0.368

3.103***

0.479

8.268***

0.382

3.306***

0.402

3.075***

Constant（bus）-1.464-10.43***-3.998-5.602***-1.002-6.719***-0.835-5.157***

Constant（car）-1.450-15.54***-0.094-9.723***-1.753-23.64***-1.932-23.39***Constant（bike）-17.90-0.092-131.9-10.58***-17.81-0.413-23.08-0.039

N

4,362

300

6,586

3,175

Log likelihood (Initial)

-5253

-358

-4083

-3441

Log likelihood (Final)

-2258

-189

-2123

-1741

Rho squared

0.570

0.472

0.480

0.494

Adjusted rho squared

0.569

0.452

0.478

0.492
*p<0.10**p<0.05***p<0.01

Change in Value of Time/Crowdedness
業務(business)
平日(weekday)

休日(holiday)

2019

2020

2019

2020

時間価値 (円/min)Value of time

11.08

29.67

23.85

-9.88

混雑の金銭価値(円/ピーク時間ダミー）
Value of crowdedness

-2.76

-9.73

0.89

10.07

非業務(non-business)
平日(weekday)
時間価値(円/min)Value of time

休日(holiday)

2019

2020

2019

2020

10.93

12.68

10.32

12.82

0.21

-1.00

0.615

混雑の金銭価値(円/ピーク時間ダミー)-2.59
Value of crowdedness
非業務は時間価値・混雑価値ともに上昇
Both time value and congestion value increased for non-work

14

Policy Implications
時間価値がコロナ禍では上昇

Increase value of time during the pandemic

コロナ後にテレワークなどが導入 Introduce teleworking during the pandemic→移動を嫌がる人が増加 Increase the people who hesitate to make a trip
→コロナ収束後も時間価値はコロナ前よりも上昇している可能性大It is likely that value of travel time after the pandemic is longer than that during the pandemic.

人々の移動が少なくなる土地利用
Land use that decreases the distance that people move

•居住地の移動 Moving the place of residence•オフィスの移動 Moving the place of office•サテライトオフィス Satellite office

混雑を回避する政策
Policy that decreases the crowdedness of public
policy implication
Dynamic
ダイナミックプライシング pricing
・鉄道 train・バス bus

15

Limitations•自転車のデータ少ない
The number of bike data is few.

•自転車/乗用車の代替手段生成可否がほぼ全て可能になっていた
Almost all "Whether alnatives can be generated Bike and Car " were generated .

•自転車や車を利用できないのに利用しないという選択をしたことにFor choosing not to use bicycles or cars when they are not available.

→自転車や車の定数項が過小評価されている可能性あり
The constant term for bicycles and cars may be underestimated.

•Nested logit modelで私有・公共交通の選択と更なる手段選択の二段階モデルのほうが適切か？
Would a two-stage model of private/public transport choice and further means selection
be more appropriate in a Nested logit model?

•混雑を表すものとして、ピーク時間帯を用いた。
The peak hour was used as a measure of congestion.

→時間帯別の混雑度のデータなどを用いる。
Data on the level of congestion by time of day and other data are used.

16