2024/9/13(金)BMSS Final presentation

松山市中心市街地における歩行者空間整備が交通手段選択に与える影響
The Influence of Pedestrian Space Development on Transportation Choicesin the Central Area of Matsuyama City筑波大学 Team Tsukuba
Yusuke Tamura Miaoheng Wei
Ruka Nakazawa Shun Nakajima Riku Nakayama

Background・Walking along Hanazonomachi Street andOkaido Street in Matsuyama, We had animpression that there were manypedestrians.
・A pedestrian space in front of Matsuyama
Station is also scheduled to be developed.

Study of the impact ofpedestrian space development
on transportation choices

Hanazono-machi Street （2024/8/1）

Renewal Plan in front of Matsuyama Station 2

Policy Analysis
Hanazono-machi Street and Main
Street are well maintained
Matsuyama City Station Area

Dougo

Maintenance was done in front ofDougo Onsen Station
Dougo Area
Renewal will take place in the future
Matsuyama Station area
Basic analysis was conducted foreach of the above three areas.

City Station
Matsuyama Station
3

Data Aggregation
Data：trip.csv

within-area
travels

Proportion of Transportation Choices by Area

City Station
Matsuyama Station

Dougo
whole data set
The proportions of pedestrian choicesin the City Station and Dougo areas are higher thanit in the Matsuyama station area.

4

Data Aggregation
Entry-mode and Within-area mode

Data：trip.csv

DougoCity Station

MatsuyamaStation5

Analysis of comments of the links
Data：entry.csv↑An example of a negative comment
Result of morphological analysis with MeCab

Based on the references(Kobayashi, 2005) and(Higashiyama, 2008), a
dictionary is created toclassify words into Positive,Negative, and Neutral
categories.

Using the dictionary, each comment is rated on a scale from -1.0 to 1.0.
(-1.0: very negative, 1.0: very positive).
Take the average of the ratings for these comments separately for positive andnegative values, and the result indicates the rate of positive and negative
impressions of the link.
6Basic Analysis
Data：entry.csv
Example:“ツタヤにてCDをレンタルしました。
ココは古めのタイトルが充実してるので、昔の曲を聴きたい時、助かります。”( I rented a CD at Tsutaya. This
place has a great selection of older
titles, so it's really helpful when I
want to listen to old songs. )" →0.667 positive ”
negative(-1)

Many comments in areas where commercial
facilities are concentrated.

(0)

positive(+1)

7Basic Analysis
Data：entry.csv

36%
58%
6%

22%

41%

negative" →0.667 positive ”

33%
53%

37%

positive

Example:“ツタヤにてCDをレンタルしました。
ココは古めのタイトルが充実してるので、昔の曲を聴きたい時、助かります。”( I rented a CD at Tsutaya. This
place has a great selection of older
titles, so it's really helpful when I
want to listen to old songs. )14%

negative(-1)(0)

positive(+1)

neutralMany comments in areas where commercial
facilities are concentrated.

8Basic Analysis
Add values to the nearest link fromeach point
Calculate the average and generate
impressions for each linknegative(-1)

Many comments in areas where commercial
facilities are concentrated.

(0)

positive(+1)

9Model Formulation
Multinomial logit model (within-trip mode choice model)
𝑈𝑈𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑏𝑏1 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑐𝑐𝑐𝑐𝑐𝑐) + 𝑚𝑚1 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑐𝑐𝑐𝑐𝑐𝑐)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏2 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏3 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)
𝑈𝑈𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 =𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 + 𝑓𝑓1 ⋅ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑓𝑓2 ⋅ 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁+𝑚𝑚2 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)

10Model Formulation
Multinomial logit model (within-trip mode choice model)
𝑈𝑈𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑏𝑏1 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑐𝑐𝑐𝑐𝑐𝑐) + 𝑚𝑚1 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑐𝑐𝑐𝑐𝑐𝑐)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏2 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏3 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)
𝑈𝑈𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 =𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 + 𝑓𝑓1 ⋅ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑓𝑓2 ⋅ 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁+𝑚𝑚2 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 : average speed of this mode × trip’s travel distance11Model Formulation
Multinomial logit model (within-trip mode choice model)
𝑈𝑈𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑏𝑏1 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑐𝑐𝑐𝑐𝑐𝑐) + 𝑚𝑚1 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑐𝑐𝑐𝑐𝑐𝑐)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏2 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏3 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)
𝑈𝑈𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 =1, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏 𝑐𝑐𝑐𝑐𝑐𝑐
�0, 𝑛𝑛𝑛𝑛𝑛𝑛 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡

𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 + 𝑓𝑓1 ⋅ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑓𝑓2 ⋅ 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁+𝑚𝑚2 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)
1, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
�0, 𝑛𝑛𝑛𝑛𝑛𝑛 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 : average speed of this mode × trip’s travel distance12Model Formulation
Multinomial logit model (within-trip mode choice model)
𝑈𝑈𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑏𝑏1 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑐𝑐𝑐𝑐𝑐𝑐) + 𝑚𝑚1 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑐𝑐𝑐𝑐𝑐𝑐)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏2 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)

𝑈𝑈𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑏𝑏3 + 𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)
𝑈𝑈𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 =1, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏 𝑐𝑐𝑐𝑐𝑐𝑐
�0, 𝑛𝑛𝑛𝑛𝑛𝑛 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡

𝑑𝑑1 ⋅ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 + 𝑓𝑓1 ⋅ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑓𝑓2 ⋅ 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁+𝑚𝑚2 ⋅ 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)
Impressions of trip 𝑖𝑖 =1, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡∑ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑜𝑜𝑜𝑜 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑝𝑝𝑖𝑖
�0, 𝑛𝑛𝑛𝑛𝑛𝑛 𝑏𝑏𝑏𝑏 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑝𝑝𝑖𝑖

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 : average speed of this mode × trip’s travel distance13

Results
Multinomial logit model (within-trip mode choice model)
5%有意𝑏𝑏1
𝑏𝑏2
𝑏𝑏3

𝑑𝑑
𝑓𝑓1

𝑓𝑓2

𝑚𝑚1
𝑚𝑚2

ネガティブの影響は強く働く→ネガティブ要素の改善が効果的
The positive influence is having a negative effect.
→ Does having more positive comments lead to alower utility of walking??
ポジティブの影響が負に働いてしまっている→推定がうまくいっていない？？
The negative influence is strong.
→ Improving negative factors is effective.
尤度比は0.839と高い→よく再現できている
The likelihood ratio is 0.839, which is high.
→ It is well-replicated (well-fitted).

14

Results
Main results：Negative image has a strong influence on mode choice→We should clean up a negative image more than make a positive image
Ex. Parking lots near JR Matsuyama sta.

15