Introduction of Dynamic Pricing inRail Transportation in The Post Pandemic Era
Behavioral Model Summer School 2022
2022/9/25#11_utokyo_liis_b
Krittanai Sriwongphanawes
Chee Yung LO
Lee Yuting

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Background: Train Congestion in Tokyo Metropolitan Area

2

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Background: Train Congestion in Tokyo Metropolitan Area
Objective:
Compare people’s changes in railway price elasticity between 2019 and 2021.
Apply the time-based fare system (increasing fare in peak hours).
Estimate and evaluate the policy’s effectiveness.

3

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Basic analysis
We applied Toyosu PP data because it includes data before and after covid19.

4

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Basic analysis
2019 (Before COVID-19)

2021 (After COVID-19)

Based on the departure time distribution of railway trips, we set “7am-9am” and “5pm-7pm” aspeak hours for railway.

5Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Basic analysis
During peak hours, most of the railway passenger are commuters.
2019 (Before COVID-19)*Unknown occupation is removed here

2021 (After COVID-19)

6

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Basic analysis
2019 (Before COVID-19)

2021 (After COVID-19)

7

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Basic analysis
If the same group of people (n=150) is traced from 2019 to 2021:
2019 (Before COVID-19)

2021 (After COVID-19)

8

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Multinomial Logit Model

Utility Function

9

BackgroundBasic Analysis

Model

Model Result

Policy Application

Discussion

Model result

2019

coefficient

Coefficient value

T-value

ASC_rail

1.650416.7323

ASC_bus-0.0932-0.8501

ASC_car-1.1160-17.9950

ASC_bike-0.5087-8.7501

Travel time

-1.9273-29.3221

Fare-0.0032-18.5219

peak*Fare0.00084.9272young

0.81956.3294

middle_age-0.6854-11.0988

is_weekday

0.8468

11.3371

N = 15148
L(0) = -19706.18
LL = -12213.93
Rho-square = 0.3802
Adjusted rho-square =0.3797

10Background

Basic AnalysisModel

Model Result

Policy Application

Discussion

Model result

2021

coefficient

Coefficient value

T-value

ASC_rail

1.803725.6000

ASC_bus0.890311.3000

ASC_car-1.2837-30.4886

ASC_bike-0.3032-7.8495

Travel time

-3.7123-57.9741

Fare-0.0058-38.1840

peak*Fare0.00010.7279commute*Fare0.002314.7197young

1.622511.8680

middle_age-0.7309-17.7384

is_weekday

0.1752303059N = 29900
L(0) = -37371.59
LL = -26045.62
Rho-square = 0.3031
Adjusted rho-square = 0.3028

11Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Model result
Aggregated price sensitivity
Multinomial Logit
YearPeakOff-peak2019-0.1322-0.2295

2021-0.3943-0.4933

12

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Policy application
How much should rail company increase the fare after Covid-19?

2021 MNL model

apply dynamic
pricing withdifferent
increasement

calculate shareand revenue

calculate newshare and revenue

compare before and after policy

share = the average probability of choosing rail
revenue = the sum of rail fare of all individuals who chose rail

13

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Policy application
Increasement in farefor peak trip (%)

Change in rail share[Comparing to beforepolicy] (%)

Change in revenue[Comparing to before policy](%)

5-0.771.40

10-1.56

2.84

15-2.37

4.18

20-3.19

5.29

25-4.025.87

30-4.85

6.60

40-6.53

7.40

50-8.22

7.77

14

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Policy application

15

Background

Basic Analysis

Model

Model Result

Policy Application

Discussion

Discussion●People are more sensitive to rail transportation’s price after covid○ They prefer more safety modes such as walking, biking and driving●Our results suggest that…
○ price increment should be applied during peak hours○ The increasement in price should lesser than around 47% of current fare in order togain revenue more than losing share●However, our model’s accuracy is low and simple○ more accurate model should be investigated○ use more advanced models to corporate unobserved heterogeneity → Mixed logit

16