Pedestrian Route Choice | 20.303 Urban Analysis

20.016 Urban Analysis

PEDESTRIAN ROUTE CHOICE BUGIS JUNCTION Clifford Mario Kosasih (1000294) Goh Pei Xuan (1000286) Ho Jia Jia Sharon (1000091) Neo Jun Hao Kevin Josiah (1000133) Oor Eiffel (1000293)

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TABLE OF CONTENTS 1. INTRODUCTION 3 2. LITERATURE REVIEW 4 3. RESEARCH QUESTION 4 4. HYPOTHESES 5 5.SAMPLE COMPLETED SURVEY 6 6. SURVEY DAY 8 7. DESCRIPTIVE STATISTICS 9 8. HISTOGRAMS 9 9. SOCIO-ECONOMIC INDICATORS 10

10. ROUTE ATTRIBUTES 11

11. SAMPLE DIGITIZED SURVEY 13 12. EXCEL CHOICE MODEL 14 13. CONCLUSION 17 14. DESIGN RECOMMENDATIONS 18 15. BIBLIOGRAPHY 19

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1. INTRODUCTION Given the complexity of path networks and pedestrian behaviour in shopping districts, it is interesting to understand the correlation of walkability and familiarity in such a context. The pedestrian route choice experiment allows us to explore our understanding of pedestrian movement and analyse the characteristics of routes between given origins and destinations relative to their familiarity of the area. This experiment was implemented in the locality of Bugis to understand due to its environmental and experiential qualities as a shopping district. To investigate this topic, spatial analysis methods were utilised to determine statistical correlations between key route attributes such as distance, shading, commercial density and number of turns.

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2. LITERATURE REVIEW Existing studies aim at understand the topics of walkability, path selection and route preferences, in relation the the perceptual experience in the urban environment. In order to better understand walkability, conceptual frameworks split overall walkability into three categories (physical features, urban design qualities, individual reactions). (Ewing & Handy, 2009) Subsequently, links were established between urban design qualities such as imageability and enclosure, into physical features such as height and proportion. This helps to decontextualise abstract features into measurable physical characteristics.

These approaches to understand pedestrian movement in the urban context comprise attempts to operationalise perceptual urban qualities through this means. These methods begin by testing the correlation between walkability and walking behaviour, followed by attempts to measure walkability through the development of Walkability Indexes based on operationalised perceptual urban qualities. (Park, Deakin & Lee, 2014) This creates an objective model giving a measurable understanding of urban environment through qualitative measurable indexes. (Ewing & Handy, 2009) Other studies investigate route selection criteria and preferences provide information on possible trends in human preferences during route selection. (Golledge, 1995) Studies also seek to understand the basic characteristics of walking trips and pedestrian movement through probabilistic means, in order to fully appreciate walking behaviour in relation to urban experience. (Tirachini, 2015) This encourages architects and urban planners to make more precise and informed design decisions to improve urban design and experience, that might improve issues such as congestion and accessibility.

3. RESEARCH QUESTION To understand how pedestrians’ familiarity of the region affect their path selection in a central shopping district.

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4. HYPOTHESES Our first hypothesis was that pedestrians are more likely to choose paths that are the shortest. This hypothesis was based on the research done to understand the probability distribution of walking trips. (Tirachini, 2015) In the research, the probability distribution of walking trips in various cities reflect an exponential decrease in number of trips with an increase in trip distances.

Our second hypothesis was that pedestrians tend to choose paths with fewer turns. Paths with more turns consequently result in more decision points for the pedestrian. A decision point is defined as a street intersection where a pedestrian decides whether to continue in the same direction of to change it. (Garbrecht, 1970) Based on the research to understand route selection criteria (Golledge, 1995), turns introduce substantial differences in the path selection criteria.

This implies greater complexity, which we believe pedestrians would avoid.

Our third hypothesis was that pedestrians who have the intention to shop would most likely select routes with higher commercial density, hence deviating from the shortest path. This is based on the assumption that shoppers walk based on their perceived shopping experience, resulting in the selection of routes without consideration of the shortest distance.

Our fourth hypothesis was that pedestrians who are more familiar with the region tend to choose paths with more shade. Given Singapore’s climate, we believe that familiar pedestrians are more calculated in their path choices and are more inclined to select routes that provide that with a more comfortable experience.

Our fifth hypothesis was that pedestrians who are more familiar with the region would be more comfortable navigating around themselves. This was based on the complexity of the shopping district in Bugis, with a significant number of path intersections in areas such as Bugis Street. We believe that this physical attribute has a significant impact on the perceived micro-level walkability in Bugis, which would improve with familiarity. Micro-level walkability can be measured by the street level physical attributes that are directly perceived by pedestrians and thus could influence their walking experience in a significant way. (Park,Deakin & Lee, 2014) 5

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5. SAMPLE COMPLETED SURVEY

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6. SURVEY DAY We decided to carry out the survey near the Entrance/Exit C of Bugis MRT station. We did the survey on Saturday, 7 February 2015 at 3:00 PM. There were four of us who carried out the survey: one surveyor stood near the junction between Rochor Rd and Victoria St to capture pedestrians from the north side of Bugis, another surveyor stood in front of Topman/Topshop to capture pedestrians from the south side of Bugis, and two surveyors stood directly in front of the MRT station exit and BHG to capture pedestrians from both ends as well as those exiting Bugis Junction.

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7. DESCRIPTIVE STATISTICS People are generally willing to walk within the distance of 150 - 300m. The willingness to walk decreases as the route length increases above 450m. The longest route of 791.4m was taken by a shopper exploring the vicinity for the first time.

The deviation from the shortest path generally falls between 0-20%. Reasons given by these surveyees include: less traffic, shortest known path, clear sight of destination, sheltered, air-conditioned, easiest route, shortest route, and habit.

Deviations from the shortest path

Route length Minimum

95.3 m

Minimum

0%

Maximum

791.4 m

Maximum

329%

Average

349.1 m

Average

8%

Median

318.9 m

Median

4%

Table 1 Route Length

Table 2 Deviations from the shortest path

8. HISTOGRAM Deviation from shortest path(%)

Route length (m) 35

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Number of pedestrian

Number of pedestrian

18 14 12 10 8 6 4 2 0

30 25 20 15 10 5 0

0-1501

150-300

300-4504

450-600

Route length in metres Graph 1 Route length (m)

600-7507

750-900

0-20

20-404

40-60

60-808

80-100>

100

Deviation from shortest path Graph 2 Deviations from the shortest path (%)

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9. SOCIO-ECONOMIC INDICATORS The respondents fall mainly in the age group of 18-35, which was also observed to be the majority of the people that visited the Bugis district. The survey was carried out on a Saturday afternoon, which was typically when working adults and schooling youth visit Bugis to shop or eat. Some of the more commonly visited places included Bugis+, Bugis Village and the National Library, which are more popular amongst the younger generation.

Also, it was observed that pedestrians over the age of 65 tend not to agree to being surveyed, which could be due to language difficulties as our survey was designed in English.

Graph 3 Percentage Distribution of Respondentes based on Age Group

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10. ROUTE ATTRIBUTES The survey aims to study the route choices of pedestrians who are more familiar with the region and the factors that affect their choice. Four route attributes were identified based on the characteristics of Bugis: 1. Turns or directional change 2. Shortest distance 3. Shaded/climately controlled space 4. Involves passing by commercial activites.

Bugis has a rather regular street network layout, which typically involves several substantial changes in direction to get from a point to another. This would affect pedestrians’ route choices as we expected them to prefer walking a straight and direct route than one with more turns. The distance of the chosen route is also significant to the pedestrian, as the typical pedestrian would want to take the fastest path that possibly requires the least amount of effort. However, this is dependent on the objective of the trip, as if the pedestrian’s aim is to shop, then there would be more deviations and result in a longer route distance.

In addition, due to Singapore’s climate, pedestrians would be concerned with the amount of shade present in the route taken. This is especially important during the time of the survey, since the surveys were conducted during the day. It was predicted that pedestrians would choose routes with more shade or walk indoors. Also, Bugis is a central commercial district with many shopping malls and smaller shops along the streets. This affects the path choice as some pedestrians visit Bugis to shop, which implies that the number of commercial activities highly influences the route selected.

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20 15 10 5 0

1

2

3

4

5

Number of Respondents

Number of Respondents

Level of Importance of Shade on Route Choice 20 15 10 5 0

Level of Familiarity

10 5

2

3

4

Level of Familiarity

5

Number of Respondents

15

1

2

3

4

5

Level of Importance of Passing by Commercial Activities on Route Choice

20

0

1

Level of Familiarity

Level of Importance of Number of Turns on Route Choice Number of Respondents

As level of familiarity increases, people place more importance on choosing routes with shorter distances and routes that are shaded.

On the other hand, our investigation has shown little correlation between familiarity and the two other key route attributes; commercial density and number of turns.

Level of Importance of Shortest Distance on Route Choice

20 15 10 5 0

1

2

3

4

5

Level of Familiarity

Graph 4 Level of Importance of the route attributes: distance, shade, number of turns and passing by commercial

activities against the level of familiarity

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11. SAMPLE DIGITIZED SURVEY Sample Survey #33 Origin: Burlington Square Destination: Bugis MRT Total Distance (m) Covered Distance (m) Covered Ratio No. of Turns No. of Commercial Activities per 100 m

Collected Route

Shortest

Alternative Route

675.3

506.4

537.3

406.1

165.9

320.5

0.60 12

0.33 5

0.60 20

30

69

70

Table 3 Digitized survey #33

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12. EXCEL CHOICE MODEL After we compiled the data to an Excel file, we are able to obtain the beta coefficient values for the four different attributes: distance, cover, turns and commercial. As we can see from the data, there are implied relationships between the walkability and the route attributes: • Pedestrians tend to be more willing to walk the route with longer distance • Pedestrians tend to be more willing to walk the route with more cover/ shade • Pedestrians tend to be less willing to walk the route with more turns • Pedestrians tend to be đ?›˝đ?›˝ more willing to walk the route with with more đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? = 206.0 commercial activities per 100 m đ?›˝đ?›˝đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘

However, it is rather odd that from the data collected, pedestrians tend to walk more given a longer distance. Therefore, we have decided to introduce đ?›˝đ?›˝đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘&#x;đ?‘&#x;đ?‘›đ?‘›đ?‘›đ?‘› another coefficient and make it an= interacted U-function. The rationale −13.4 đ?›˝đ?›˝đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘ behind this is that we observed a trend where shoppers in Bugis area tend to deviate from the shortest path in order to visit shops along the way.

Therefore, the additional coefficient should reflect the fact that some of the đ?›˝đ?›˝đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? = 14.6 method is introduced so as to respondents’ are in Bugis to shop. A binary đ?›˝đ?›˝đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘ differentiate between shoppers and non-shoppers. đ?‘ˆđ?‘ˆ = đ?›˝đ?›˝0 + đ?›˝đ?›˝đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘ Ă— đ??ˇđ??ˇ + đ?›˝đ?›˝đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ą Ă— đ?‘‡đ?‘‡ + đ?›˝đ?›˝đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? Ă— đ??śđ??ś đ?‘ˆđ?‘ˆ = đ?›˝đ?›˝0 + đ?›˝đ?›˝đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘ Ă— (1 + đ?›˝đ?›˝đ?‘ đ?‘ â„Žđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œđ?‘œ Ă— đ?‘†đ?‘†) Ă— đ??ˇđ??ˇ + đ?›˝đ?›˝đ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ąđ?‘Ą Ă— đ?‘‡đ?‘‡ + đ?›˝đ?›˝đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? Ă— đ??śđ??ś

Beta coefficient

Value

Distance

0.006847

Cover

1.34353

Turns

-0.08837

Commercial

0.09534

Table 4 Beta coefficient for the route attributes

Beta coefficient

Value

Distance

0.006563

Cover

1.3518

Turns

-0.08804

Commercial

0.09558

Shoppers

0.04575

Table 5 Beta coefficient for the route attributes after interaction

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It can be observed that the beta coefficient for distance is reduced so that it gravitates towards 0. This means that there is a desirable effect on the introduction of the interacted U-function. Nonetheless, there is still a positive relationship between distance and walkability. This may be due to the fact that there are other reasons for people to not walk along the path with the shortest distance to their destinations: • some pedestrians that we surveyed wanted to avoid the Saturday afternoon crowds in Bugis • the sample size may be too small for us to see a more general trend From the model, we observe that the benchmark model has a pretty low value of the sum of logs-likehood beta, -43.658.

When the solver is run and the model is maximized with all the coefficients, the LL-beta value increases to be -40.010.

This shows that the model has efficiently maximized this value in order to get all the beta coefficient values.

In addition to that, we have tried the model with excluding the specific attirbutes one by one. This shows which attributes have more impact to maximizing the optimal model.

Model

Sum of logs-likelihood (LL-beta)

Benchmark model

-43.658

Without distance attribute

-43.175

Without cover attribute

-40.717

Without turns attribute

-41.290

Without commercial attribute

-41.232

Full model

-40.010

Table 6 Various Excel choice model

Ranking of the route attributes

1. distance 2. turns 3. commercial 4. cover

List 1 Ranking of the route attributes

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𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 206.0 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 206.0 Pedestrians will walk 206.0m more in order to get 1% more cover in the path. 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽 𝛽𝛽𝑡𝑡𝑡𝑡𝑟𝑟𝑛𝑛𝑛𝑛 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = −13.4 = 206.0 𝛽𝛽 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑡𝑡𝑡𝑡𝑟𝑟𝑛𝑛𝑛𝑛 = −13.4 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝛽𝛽 = 14.6to avoid one turn.

Pedestrians will walk 𝑡𝑡𝑡𝑡𝑟𝑟𝑛𝑛𝑛𝑛 13.4m=more −13.4 𝛽𝛽 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 14.6 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶 𝑈𝑈 = 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝛽𝛽× 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 14.6 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑈𝑈 = 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 × 𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶 Pedestrians will walk more in× order to pass×by commercial activity every (1 + 𝛽𝛽𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑆𝑆) 1×more 𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶 100m. 𝑈𝑈 =14.6m 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑈𝑈 = 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 × 𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶 𝑈𝑈 = 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 × (1 + 𝛽𝛽𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 × 𝑆𝑆) × 𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶 𝑈𝑈 = 𝛽𝛽0 + 𝛽𝛽𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 × (1 + 𝛽𝛽𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 × 𝑆𝑆) × 𝐷𝐷 + 𝛽𝛽𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × 𝑇𝑇 + 𝛽𝛽𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶

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13. CONCLUSION From the analysis of the data collected, we have found that the distance is the most significant factor affecting pedestrians’ choice of route. This is followed by the number of turns, number of commercial activities and lastly, the amount of shade present through the path. These findings have proven all our initial hypotheses correct.

Several challenges were faced during the data collection, such as the respondents not being able to remember their exact route and hence the data collected was not as accurate. Majority of the respondents fell in the age group of 18-35 years old, as the older people were less willing to participate in the survey, which may not be a complete representation of the demographic present in Bugis. To rectify these problems, we could request for permission to follow the respondent before the survey was carried out and personally mark out the route accurately. Additionally, we could lengthen the total time allocated for data collection to ensure that we receive an adequate number of surveys for each age group.

Number of Respondents

Challenges Faced

Graph of Respondents' Comfort Level of Navigating Around against Level of Familiarity 20 15 10 5 0

1

2

3

4

5

Level of Familiarity

Graph 5 Graph of respondents’ comfort level of navigating

around against level of familiarity

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14. DESIGN RECOMMENDATIONS These design improvements were suggested after taking into account the route choices by our surveyees and the importance of certain characteristics of the path towards them. Some of the characteristics which were important to them were actually missing from their route and hence, we tried to come up with some solutions that would hopefully help incorporate these ‘missing’ characteristics into their route choice.

1. Widening of walkways It would be a good idea to widen the walkways and passageways so that people will feel more comfortable walking along that route as it will not be too crowded. Many people actually based their route choice on the fact that there is lesser human traffic along that route. Besides that, some surveyees also indicated that they would rather use the overhead bridge (connecting from the second level of Bugis+ to Bugis Junction - leftmost image) as it is less crowded there, even though this meant that they would actually be walking a longer distance than intended. As having the shortest distance to walk is ranked highly in most people’s choices, they would ultimately still choose to cross over from Bugis Village to Bugis Junction using the road crossing.

Hence, it results in a huge bottle-neck at the walkway in the middle of the road (rightmost image). In order to curb this issue, we would suggest widening the walkway so that it would be able to accomodate more people and they would not have to take the alternate longer route of walking over to use the overhead bridge for crossing.

Figure 1 Pedestrian crossing in front of Bugis Street

Figure 2 High pedestrian flow at the Bugis Street crossing

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2. Positioning of overhead bridges at more suitable places As most people actually indicated that their top two concerns were actually having a shaded route and the shortest route, we suggested shifting the overhead bridge to a more appropriate place such that people could enjoy a shorter and shaded walk from Bugis Village to Bugis Junction (common origin and destination).

Figure 3 Bugis+ overhead pedestrian bridge

Figure 4 Proposed new location of overhead pedestrian bridge

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3. Having more commercial activities along the streets and roads As Bugis area is known for the shopping and food, most people actually based their route choices on the amount of commercial activities they would pass by, such as food stalls/restaurants o r some of their favourite shops in the shopping malls. Hence, it would be a good idea to include more shops along the road where passerbys could also enjoy the window shopping or even grab a quick bite at at food stall. This would encourgae walkability in that area as people would feel more engaged while walking and there would be interesting things to catch their attention and they would be entertained by the various different shop fronts as well.

Figure 5 Shops along the underground

4(a). Making the entrance to underpass more prominent We realised that not many people actually know about the existence of the underpass there and hence avoided using it unintentionally. We should aim to make the entrance of underpasses more interesting and obvious such as adding a shop there or having certain exhibitions.

Figure 6 Case study of underground shops along

pedestrian route in Holland Village

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4(b). Provision of more shops along the underpass According to some responses from the surveys, most people actually do not use the underpass that is available near the MRT even though it is a sheltered and shorter route compared to the one that they took while getting from the origin to the destination. This might be due to the fact that the underpass does not have many commercial activities there and hence, there would be nothing much for the pedestrians to look forward to while walking there. Hence, adding more shops would be a good incentive for people to utilise the underpass as it would liven their experience.

Figure 7 More shopping activities along the underpass

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15. BIBLIOGRAPHY Gale, N. D., Golledge, R. G., Pellegrino, J., & Doherty, S. (1990). The acquisition and integration of neighborhood route knowledge in an unfamiliar neighborhood. Journal of Environmental Psychology, 10(1), 3–25.

Golledge, R. G. (1995). Path Selection and Route Preference in Human Navigation: A Progress Report. In G. Goos, J. Hartmanis, & J. van Leeuwen (Eds.), (pp. 207–222). Vienna, Austria: Springer.

Garbrecht, D. (1970). Frequency Distribution of Pedestrians in a Rectangular Grid. Journal of Transport Economics and Policy, 4(1), pp. 66–88.

Hess, P. M., Moudon, A. V., Snyder, M. C., & Stanilov, K. (1999). Site design and pedestrian travel. Transportation Research Record, 1674, 9–19.

Park, S., Deakin, E., and Lee, J. S. (2014). Developing Perception-based Walkability Index to Test the Impact of Microlevel Walkability on Sustainable Mode Choice Decision.

Tabor, P. (1976). Analyzing Route Patterns. In L. March (Ed.) The Architecture of Form. Cambridge: Cambridge University Press.

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