TDM 2008 in Wien, 16-18 July 2008

TDM Experiment of Railway and a Shopping Centre using Smart Card System Yasuo Asakura, Asakura Takamasa Iryo Iryo, Yoshiki Nakajima, Kaoruko Sugita, Sei-ichi Kitano, Kobe University JAPAN

Railway Smart Card Systems in Japan • ICOCA off JR (J (Japan R Railway) il ) WEST • SUICA of JR EAST • PiTaPa (Postpay IC for Touch and Pay) system of Private Railway Consortium in Kansai Area

Possibility of Smart Card Data (1) Smart Card Data Railway Companies Long g Term Trend of Passengers

Longitudinal Data Analysis)

New Services （Fare & Diagram） Increase of Demand and Revenue

Empirical p Studies on Transport & Marketing Science Transport Researchers

Frequency of Smart Card Use

Number of Users (IDs)

Weekend d (trip/day y)

W kd (trip/day) Weekday ( i /d )

W kd (trip/day) Weekday ( i /d )

Period： June 2007， I d Index：Averaged A dF Frequency off Smart S t Card C d Use U in i aD Day（trip/day） （t i /d ） Number of Samples：198,240

Possibility of Smart Card Data (2) S Smart t Card C dF Function ti Fare of Public Transport

General Goods & Services

Combined Services & Policies using Smart Card

Increase of Railway Use

Promotion of Shops and Areas

Compact and Active Cities with less Environmental Load

Background • A downtown shopping centre usually provides free car parks k for f customers t who h come by b automobiles. t bil • But rarely makes reimbursements for those who use public transport. This is because the verification of boarding on public transport is not easy without any complex procedures. procedures y makes possible p to verify y the • A smart card system customer’s boarding on public transport. • This could be incorporated into the TDM (Travel Demand Management) scheme of public transport collaborating with shopping activities as one body.

Objectives • This study aims to propose a collaborative TDM scheme in which a shopping centre provides incentives for customers who intend to use railways. • A field experiment was carried out in collaboration with a railway company and a shopping centre centre. • This study also aims to show the travel behaviour of customers who h jjoined i d the h experiment. i • In addition to the questionnaire survey survey, the railway smart card data of customers are analyzed to know their longitudinal railway use.

Collaborative TDM Scheme Subsidy

Public Sector

Subsidy

Revenue Redistribution Shopping Centre or Areas

Railway Company

Railway Fare

IIncentives ti Incentives Customers

Purchase Goods or Services

Field Experiment •

Field experiment was held at a shopping centre located 5 minutes walk from a railway station.

•

The floor space is more than 34 34,000 000 m2.

•

The averaged number of customers is about 22,000 persons per day.

•

Capacity of car park is about 3,000 vehicles.

•

You will have a 2 hours free car p park ticket when you buy more than 2,000 yen.

Preliminary Survey (# of samples 500) 600 500 400

S m art C ard H olders N o S m art C ards T otal

300 200 100 0 R ailw ay

• • • • •

A utom obile

W alk & B icycle

T otal

Shares of railway and automobile were 16% and 49%. Smart card holders were 30% of all customers. Targeted customers were the smart card holders who actually came by railway. This was just 4%. 37% of automobile users has a smart card. There are a large number of potential customers who could come by railway.

Design of TDM Experiment Publicityy Work (Posters, WEB information)

Shopping S opp g Ce Centre eo or Areas eas

Railway Company

Railway R il Fare

Free Coupon Customers

Purchase P h Goods or Services

• A 500 yen ffree coupon was given i to the h customer who h spent more than 2,000 yen. • The incentive was provided by the shopping centre centre. • The railway company supported the publicity work.

Smart Card Information • A customer t was required i d tto show h hi his/her /h ID iinformation f ti of the smart card for validating the railway use. • The record information involves the date and time of railwayy use, the names of the on and off board stations. • The behavioural data were also collected with questionnaire i i survey. • Th The allowance ll off analyzing l i th the hi historical t i l personall record d of railway usage was also confirmed. All these data were gg g and anonymously y y analyzed. y aggregated

Assumption on Behavioural Change Activityy

Shopping Other Activities

Travel Mode

Destinatio n

Railway

Shopping Centre Other Shops

Other Modes

Stay at home Participants were asked the alternative activities, destinations and travel modes if the field experiment was not held.

Actual Behavioural Change Shopping Purpose (7)

Other Activities

Railway

Shopping Centre Destination (13)

Mode ((27))

Other Shops

Other Modes

Generation (12)

Stay at home Alternative Behaviour Visit here as usual

127

66 %

No behavioural change

Visit here, but other travel mode

27

14 %

Travel mode change

Visit other shopping centre

13

7%

Destination change

Visit other places, but not for shopping

7

4%

Activity change (trip purpose change)

Stay at home

12

6%

Activity change (new trip generation)

Others or unknown

4

2%

--

190 100%

New Customers and Railway Users Shopping

Railway

Shopping Centre

Purpose (7)

Mode ((27))

Destination (13)

Other Activities

Other Shops

Other Modes

Generation (12)

Stay at home

New customers to the shopping centre = Generation (12) + Purpose (7) + Destination (13) = 32 32/190=17%

New railwayy users = Generation (12) + Mode (27) = 39

39/190=20%

Expected Frequency of Shopping Trips in the Future with behaviour change

45

w/o behaviour change h

82

0%

• • •

5

20%

29

40%

60%

80%

12

increase

15

no change unknown

100%

More than 60% answered that the frequency would increase. The p percentage g of ‘increase’ for the g group p who changed g their behaviour was greater than that of the group without behavioural change. Once a participant has actually changed his/her behaviour, he/she will make k a positive iti answer ffor the th future f t expectation. t ti

Expected Minimum Amount of Incentive for the Future Experiment minimum incentive (yen) with behaviour g change

48

79

0%

• •

7

4

500 400

w/o behaviour change

•

3

20%

12

40%

60%

18

80%

16

300 200

100%

Those who did not change their behaviour would join the experiment only if positive incentive was provided. However, 60% of them expected the same incentives. Those who actually changed their behaviour might have paid some monetary t costs. t They answered more seriously on the amount of incentives. Large monetary costs may be required when the same experiment is continuously held in the future.

Analysis of Smart Card Data Before 4 months weekday weekend k d

Experiment Nov. 2007

After 4 months weekday weekend k d

G Group 0 All S Smartt C Card dH Holders* ld *

N=3 458 N=3,458

*) those who did not join the experiment, but used the railway station near by.

Group 1 with behaviour change

N=52

Group 2 w/o behaviour change

N 127 N=127

Frequency distribution of number of smart card usage (# of trips) per day. Any differences? any changes?

freqency

freqency

0.00

num ber of journeys per day

2.50

2.25

2.00

1.75

1.50

1.25

1.00

0.75

0.50

0.25

0.00

2.50

num ber of journeys per day

2.25

0%

2.00

0% 1.75

5%

0% 1.50

5% 1.25

10%

5% 1.00

15%

10%

0.75

15%

10%

0.50

15%

0.25

20%

0.00

20%

num ber of journeys per day

2.50

25%

20%

2.25

30%

2.00

25%

35%

1.75

30%

W ithout behaviouralchange N = 127 avg.= 0.714

40%

1.50

25%

35%

1.25

30%

W ith behaviouralchange N = 52 avg.= 0.891

40%

0.75

35%

0.50

A llsm art card holders N = 3458 avg.= 0.750

40%

45%

1.00

45%

0.25

freqency 45%

Figure 5.a 5 a Number of Trips per Day (Weekday, (Weekday Before Experiment) freqency

freqency

num ber of journeys per day

0.0 00

2.5 50

2.2 25

2.0 00

1.7 75

1.5 50

1.2 25

1.0 00

0.7 75

0.5 50

0.2 25

0.0 00

2.5 50

2.2 25

0%

2.0 00

0% 1.7 75

5%

0% 1.5 50

5% 1.2 25

10%

5% 1.0 00

10%

0.7 75

15%

10%

0.5 50

15%

0.2 25

20%

15%

0.0 00

20%

num ber of journeys per day

2.5 50

25%

20%

2.2 25

30%

2.0 00

25%

35%

1.7 75

30%

W ithout behaviouralchange N = 127 avg.= 0.563

40%

1.5 50

25%

35%

1.2 25

30%

W ith behaviouralchange N = 52 avg.= 0.502

40%

1.0 00

35%

0.5 50

A llsm art card holders N = 3458 avg.= 0.519

40%

45%

0.7 75

45%

0.2 25

freqency 45%

num ber of journeys per day

Figure 5.b Number of Trips per Day (Weekend, Before Experiment)

freqency

freqency

45%

0.00

2.50

num ber of journeys per day

2.25

2.00

1.75

1.50

1.25

1.00

0.75

0.00

num ber of journeys per day

0.50

0% 0.25

5%

0% 2.50

5%

0% 2.25

5% 2.00

10%

1.75

10%

1.50

10%

1.25

15%

1.00

15%

0.75

15%

0.50

20%

0.25

20%

0.00

20%

2.50

25%

2.25

30%

2.00

25%

35%

1.75

30%

W ithout behaviouralchange N = 127 avg = 0. avg. 0 623

40%

1.50

25%

35%

1.25

30%

W ith behaviouralchange N = 52 avg = 0. avg. 0 766

40%

1.00

35%

0.75

A llsm art card holders N = 3458 avg = 00.719 avg.

40%

0.50

45%

0.25

freqency 45%

num ber of journeys per day

Figure 5.c 5 c Number of Trips per Day (Weekday, (Weekday After Experiment) freqency

freqency

45%

num ber of journeys per day

Figure 5.d Number of Trips per Day (Weekend, After Experiment)

2.50

2.50

num ber of journeys per day

2.25

2.00

1.75

1.50

1.25

1.00

0.75

0.50

0.25

0.00

num ber of journeys per day

2.50

0% %

2.25

0% 2.00

0% 1.75

5%

1.50

5% 1.25

10%

5% 1.00

10%

0.75

15%

10%

0.50

15%

0.25

20%

15%

0.00

20%

2.25

25%

20%

2.00

30%

1.75

25%

35%

1.50

30%

W ithout behavi b h ioural lchange h N = 127 avg.= 0.545

40%

1.25

25%

35%

1.00

30%

W ith behavi b h ioural lchange h N = 52 avg.= 0.384

40%

0.75

35%

0.50

A llsm art card h lders hol d N = 3458 avg.= 0.502

40%

45%

0.25

45%

0.00

freqency

Comparison of Average Values 1 0.891

0.9 0.8

0.75

0.766 0.719

0.714

0.7

0.623

0.6

0.563

0.545

0.519 0.502

0.5

0.502 0.384

0.4 0.3 0.2

A llS m art C ard H older W ith C hange W ithout C hange

0.1 0

• • •

W eekday

W eekend

W eekday

W eekend

B efore

B efore

A fter

A fter

Number of trips became smaller due to seasonal effect. Those who changed behaviour were the less frequent users in weekend but frequent users in weekday. Those who did not change behaviour were the less frequent users in weekday but frequent users in weekend.

Implication •

• •

• •

Those who Th h used d the h railway il ffrequently l iin weekday kd might i h h have hi higher h chance to get the information of the experiment, and changed travel behaviour to participate the experiment. The publicity to heavy users of railway may be effective to encourage them to generate shopping/railway trips in weekend. However such behaviour was unusual for them However, them, and might not continue after the experiment unless attractive incentives were provided. Those who used railway frequently in weekend were not necessary to change their behaviour to participate the experiment. On the other hand hand, they showed the higher frequency of the smart card usage after the experiment, and this might be the effect of the experiment.

Summary • The collaborative scheme of a railway and a shopping centre was proposed as a TDM policy of promoting shopping areas. • A smart card system for public transport and shopping activities must be essential for this collaborative scheme. • Towards sustainable systems, design strategies of i incentives ti and d rules l are more iimportant t t as th they should h ld b be not only attractive for consumers but also affordable for suppliers. • A large amount of smart card data could be useful for analyzing the longitudinal behaviour of customers and developing TDM policies in the future.