2022 年 10 巻 2 号 p. 111-130
This study examined how to generate demand for predictive user flow models, which enable designers to anticipate human activity in public spaces. Data were collected via observations, interviews, and photo analysis to assess the status quo and answer study questions. These data serve two purposes: first, to calibrate an accurate user activity pattern, based on actual data for many months, to examine the relationship between human activities and space, and second, because the data is longitudinal, to test how accurate our forecast is. If the city knows where changes in activity patterns occur and where those changes affect the physical dimension of public space, it can prioritize investments in better public areas and ask developers to contribute to better public spaces rather than broader roadways.
Ignoring the human-space interaction necessitates this study. Activities are important in the link between people and public space because they make it concrete. Different tools have been developed to understand landscape patterns. More techniques for designing and managing historic public spaces are necessary. Urban planners require new techniques to analyse public social engagement, public space spatial dimension, and people-space linkages throughout time.
The urban square's ability to support a wide range of activities makes it a place for social interaction. Although recent urban planning has made squares only nodes to alleviate traffic, public spaces have become social hubs in the past. In this regard, the relationship between activity and the social square is one of the research goals. For the research, we employed interviews and picture analysis. This study's quantitative analysis also uses graph theory and urban network indices. This study hypothesizes that social space influences various activities and patterns. More space in a public square means more meaningful activity patterns.
We used to have more public spaces because Yazd city was walkable and cyclable, but now we have a vast urban development area that requires automobiles, releasing more CO2 into the atmosphere. Healthier cities can be achieved by monitoring what happens in public spaces and how to organize various social activities that benefit all. This data also allows us to track how much walking, pushing baby strollers, jogging, and talking happens in urban public spaces like Khan Square in Iran, which is becoming more appealing and generating more walking trips. It allows for the creation of safer and more comfortable public spaces for all ages and demographics because urban planners need better data to help users plan more areas following this model.
Hillier and Hanson used graph representation and analysis to street networks, resulting in Space Syntax (Hillier, 1996; Hillier & Hanson, 1984). Their research shows that the geographical configuration of roadway networks is linked to social phenomena, including pedestrian flow, crime geography, and business distribution. Unlike other graph theory applications in transportation, Space Syntax researchers use a dual graph model, where streets are nodes and crossings are edges (Sevtsuk, 2014). In recent years, new street network graph analysis approaches have evolved. Porta and Xie have created a set of spatial graph indices (Porta, Crucitti, & Latora, 2006; Porta, Latora, & Crucitti, 2012; Xie & Levinson, 2007). The Axwoman toolbox (Jiang, Claramunt, & Batty, 1999), the SANET toolbox (Okabe, Okunuki, & Shiode, 2006; Okabe & Sugihara, 2012) and other custom-built GIS programs have all been developed to operationalize spatial network studies (Miller & Wu, 2000; Peponis, Bafna, & Zhang, 2008).
A city's built environment is often represented by an interconnected web of travel pathways that links activities and venues. The restrictions imposed by street geometry and other manufactured or natural boundaries are used to depict the world rather than idealized Euclidian distances. Standard Euclidian buffer lengths risk joining places that are genuinely separated by roads, bodies of water, or fences, resulting in an overestimation of accessibility. An exact network representation of the same region might be used for a more accurate approximation. Urban network analysis measures have been shown to predict a wide range of exciting events. Using network analysis, Porta and Sevtsuk revealed that retailers commonly locate "between" neighbouring destinations, such as jobs, transportation stops, and heavily populated regions (Porta et al., 2009; Sevtsuk, 2010). Urban economics has evolved from economic geography to economic geometry.
Similarly, alternative land uses, land prices, rentals, commercial income, and other spatial economic factors can be examined. Decades of research have explored the impact of urban highway network structure, and topology on pedestrian and vehicular traffic flows (Hillier, 1996; Hillier et al., 1986). Sevtsuk also highlighted how adding a new building or firm affects the accessibility of existing buildings or establishments by characterizing them with a third network element. It can show how much more foot traffic a new development will bring to specific Street segments or generate new commercial opportunities.
An essential flaw in present network analysis methods is their inability to generate alternative geometric configurations. Most geographical network analysis tools are good at characterizing current geometric networks but bad at recommending improvements to existing networks given constraints (Raford, 2010; Schneider, Bielik, & König, 2012). Thus, an analyst commonly uses before-and-after simulations and network analysis to show the advantages of proposed urban improvements. It is less clear how the insights may improve the design. This is a drawback of all spatial analysis approaches, not only network analysis. No new designs are created; instead, existing ones are analysed. Procedural urban models are a recent possible improvement in this field (Parish & Müller, 2001; Vanegas et al., 2012). These models can generate geometric configurations of urban form on the fly based on input parameters and exhibit the geometric outputs to get a more desired input parameter combination.
We will employ Sevtsuk's technique to describe multi-scale urban public spaces utilizing a representational framework common in transportation and social sciences (Sevtsuk, 2014).
This field includes environmental, social, and economic factors, such as improving local energy security, and people's activities and behaviours as sustainable solutions, economic co-benefits for private users, and others. In this arena, providing decision-makers with evaluation tools that can evaluate the complexity of repercussions is critical to choosing the best sustainable solutions (United Nations Department of Economic and Social Affairs, 2016). Furthermore, with 70% of the world's population living in cities by 2050, sustainable public spaces in cities will be critical to building new sustainable models since all sectors may minimize emissions, and every effort must be made. New network indices are used to discover the optimum design profile to adapt to new energy, environmental, and market rules. This research focuses on the geographical region historically rich in sustainable features and their link to people and activities, or more broadly, cities.
It is organized as follows: Soon after the introduction, a section on research methodology explains the stages involved in conducting a study. The findings section then summarizes the analytical stages. It also aims to summarize the most important findings from previous research and suggest future study directions. This review can assist future study and demonstrate the assessment method's importance in this field. This research builds a unique network-based pedestrian flow model in Yazd, Iran. Data on Yazd's urban shape, land-uses, facilities, pedestrian pathways, and weather conditions are utilized to anticipate pedestrian flows inside a sustainable urban square. Calibration allows the model to extrapolate pedestrian flows over a public place in the historical centre, with rich heritage features, and estimate how future development projects would be designed based on pedestrian flows. Cities increasingly require quantitative models to anticipate the effects of planned built-environment modifications on foot traffic (Cooper & Chiaradia, 2020; Crucitti, Latora, & Porta, 2006; Dogan, Samaranayake, & Saraf, 2018; Sevtsuk & Mekonnen, 2012; Stahle, Marcus, & Karlstrom, 2007). Walkable cities save energy (Frank & Pivo, 1994; Krizek, 2003; Newman & Kenworthy, 1999; Zegras, 2004), improve public health (Forsyth et al., 2008; Hoehner et al., 2005; Rundle et al., 2007), and create community and sustainable public spaces (Jacobs, 1961; Klinenberg, 2018; Sevtsuk, 2021). For communities to promote walking, it is necessary to determine both the overall number of walking trips and their distribution along specific roadways (Weinstein Agrawal, Schlossberg, & Irvin, 2008). Our findings imply that the built environment shapes pedestrian circulation. Changes in residential, work and amenity locations affect pedestrian flow patterns throughout the day.
We monitored Khan Square in Yazd, Iran, during weekdays and weekends for a few months using the mapping technique. Then, the activity pattern was reviewed thoroughly, analysed by graph network indicators. The analysis was content-based and carried out by the authors on medium-scale research.
The model, based on graph theory, is designed to depict the impacts of urban shape and land-use accessibility on pedestrian trips. Additionally, it takes behavioural factors into account while calculating trip lengths, decay rates, and route selection assumptions. We concede, however, that the model does not capture all of the variables affecting activities entirely. It disregards street social characteristics, cultural considerations, changes in individual needs and capabilities, and differences in the quality of sidewalks on various streets, all of which have been found to affect walking behaviour (Mehta, 2008). The key indicators and research methods usedin this study are displayed in Table 1.
Table 1. Key indicators and research methods (Hall, 1969; Scheflen, 1972; Sommer, 1969)
| Aims | Methods | Tools | Indicators |
|---|---|---|---|
| To identify the user’s actual activity patterns in the case study. | Observations | Camera | Tie-signs and social distance |
| To collect detailed descriptions of the interviewee’s activities. | Various ethnographic interviewing | Field notes | |
| To examine the spatial circumstances. | Spatial analysis | App | Degree of social involvement |
| To analyse the spatiality of the interactions. | Body language | Interview/ note taking | Body orientation |
Graph theory is a mathematical model that may be used in many fields. The mathematical model's theorems give information about each interpretation and meet its assumptions. These findings result in the translation of abstract mathematical model theorems (Harary, 2018; Harary & Norman, 1953). In the post-war period, many publications on applied graph theory aided the dissemination of graph theory among planners (Berge, 1962; Harary, 2018). This research proposes new graph geometric metrics and models for social urban activity patterns. Two spatial models are developed using a geometric graph to describe node centrality and connections. They describe the specific value of each individual and social activity related to the entire system. Graph geometry helps architects see the potential geometrics of a place. New geometric metrics and models should soon expand and widen our knowledge of architecture and urban spatial networks. A graph may describe and explain any spatial connection (Harary, 2018; Harary & Norman, 1953). A connected graph reflects, in this theory, the group communication pattern in which information is transferred from one member to another and to all members. A point's degree indicates the number of individuals the corresponding group member can interact with directly. Because there is precisely one path between each pair of points on a tree, a tree's number of points is greater than its number of lines. If an extra line is drawn between the points of a tree, the resultant graph no longer resembles a tree. In contrast, if each pair of points on a graph has precisely one path connecting them, the graph must be a tree. The distance between two connected graph points is equal to the length of the shortest path linking them (Harary, 2018; Harary & Norman, 1953; Rajabi, Noqsan Mohammadi, & Montazer Al-Hodjjah, 2020).
Figure 1. Communication patterns of a social group in urban spaces (Harary, 2018; Harary & Norman, 1953)
The sustainable public square activities are displayed as a graph (Figure 1) according to the category in Table 2, Table 3, and Table 4. Hence, each node in the graph represents an activity, and each edge reflects the connection between activities, which depicts the interactions between activities and the patterns users might encounter.
If an urban plaza hosts cultural and social activities, it will become more sustainable. Meeting new people with passive interactions (observing, listening, or talking to each other) will encourage users to stay. These activities occur spontaneously when individuals stroll together. Most space ownership comes from social activities (McLeod, Pryor, & Meade, 2004). They represent a venue for conversation since they are unbiased. The boldest activity among the meaningful activities is social interactions in a sustainable public space, Table 2.
Table 2. Interaction type’s classification and their measurement tools
|
|
Social interaction | Reference | |||||
|---|---|---|---|---|---|---|---|
| Organized Social interaction | Random Social interaction | (Dines et al., 2006) | |||||
| - | - | Sudden | Routine | (Dines et al., 2006) | |||
| Inactive | Active | Inactive | Active | Inactive | Active | (de Arruda Campos, 2012) (Architects & Gehl, 2011) | |
| Observation-photography-interview | Observation-photography-interview | Measuring tool | |||||
The public area offers fillers as amenities to facilitate social interactions; they create unique relationships between persons in space. According to Alexander (1977), the interaction territory is determined by spatial circumstances such as layout, diversity, detail, big scale, and openness rather than location and amenities. Paths in public squares are of a spatial structure that concentrates or leads people's movement by eliminating access points. In other words, pathways that offer spatial, behavioural, and sensory shifts might become favoured walking and 'people-watching' areas (Simões Aelbrecht, 2016).
Jan Gehl categorizes outdoor activities into three types, see Table 3. Squares as public areas facilitate all activity, mainly social and optional ones (Gehl, 2007). In this way, squares' edges and corners are dynamic and popular zones where people gather and converse (Whyte, 1980). Notably, Gehl (1987) argues that environments become meaningful and appealing when all activities are merged (Rasouli, 2013).
Table 3. Activity type’s classification in a sustainable public space, (Abbasian, 2016; Gehl, 1987)
| Activities | ||
|---|---|---|
| Outdoor activities | ||
| Necessary activities | Optional activities | Social activities |
| going to work, going to school, shopping, waiting for a bus | taking a walk to get a breath of fresh air, standing around enjoying life, sitting, sunbathing | meeting, conversations, childs’ play, passive contact: seeing, hearing |
As a result, the activity system, one of the most critical elements and contents of urban public spaces, is quantified by awareness of organizational structure and direct links. Thus, we evaluated how activities are carried out to gain a better understanding of these patterns. Additionally, urban activities are classified as formal or informal depending on their location; the pattern of the activities' interrelation can be examined.
Table 4. Activity type’s classification and their measurement tools
|
|
Human activities in a sustainable public space (urban square) | Reference | Ways to record activity patterns | Techniques for recording activity patterns | Activity pattern recording tools | |||
|---|---|---|---|---|---|---|---|---|
| Outdoor activities | (Architects & Gehl, 2011) (Gehl, 1987) |
- Interfering - Non-interfering |
-Observation - Interview |
- Photography - Taking notes - Mapping |
||||
| Social activities | ||||||||
| Individual activities | Group activities | (Pakzad, 2006) | ||||||
| Formal | Informal | Formal | Informal | (Parsi, 2002) | ||||
Public sustainable squares are spaces for various formal and informal social activities (daily interactions): different users share similar social and cultural characteristics, attitudes or interests, not necessarily local, leading to new formal and informal social engagement, activities, and interaction (Parsi, 2002). See Table 4.
Khan Square in Yazd, Iran, is a historical square from the Qajar era taken as a case study. The formation of the Khan complex along the Khan Bazaar was a significant part of this route and elevated its importance. This well-designed plaza is part of a larger complex known as the Khan Complex, which helped unite the city's two main squares during the Qajar period, see Figure 2 and Figure 3. The Khan complex includes a school, a bath, a square, and a bazaar. The Khan Square shops provide access to the surrounding bazaar and mosques.
Furthermore, there are meaningful social interactions due to the square position in the historical context, as the older population rate is higher than in other parts of the city, according to the demographic structure in this area. Additionally, This square's proximity to valuable cultural places such as Imamzadeh, a mosque, and a medieval bazaar draws extra attention. However, pedestrians, particularly the elderly, are declining in number due to the widespread car and motorbike use; there are too many vehicles, air/noise pollution generated by them, inadequate urban furniture, impaired square functions and user needs, and bad lighting conditions.
Figure 2. The plan of the Khan complex in Yazd- Iran
Figure 3. Khan square section (Portico as a geometric-physical factor)
We collected information on square activities by observations and field surveys. We then described and narrated the environmental-social data and user activities to classify them and map the movement of the meaningful activities with the qualitative method. Experience is relied upon to describe understanding of social activities, which means that "eyewitnesses" analyse the subject. The monitoring was carried out in two ways: static and dynamic monitoring. We used three types of activity recording in static monitoring: territory, activity effects recording (tracking), and photography. One to three cameras were shot simultaneously due to the size of the research space. It enabled us to monitor the activities with various frameworks and particular framing due to the necessity of spatial continuity.
Furthermore, facilities and equipment in the landscape were one factor that affected synthesizing the time intervals: a 5-second interval is an optimal time to record the movements since intervals longer than 5 seconds may reduce the accuracy of monitoring activities and not include the factors moving quickly in and out of the frame. See Table 7, Figure 4, and Figure 5.
Monitoring periodWe monitored the Khan square for five months; below Table 5 is a 30-day sample of our observations: 30 days between September 1 and December 1, 2019, while the temperature in Yazd ranged between 19 and 25 degrees Celsius. We observed the square over two four-hour periods: from 10:00 a.m. to 2:00 p.m. and from 4:00 p.m. to 8:00 p.m. Continuous monitoring over 30 days enabled us to identify a specific pattern from the Khan square's activity analysis. Additionally, we did activity time surveys, made notes, and coded, see Figure 6 and Figure 7. We identified the geometric-physical characteristics that contributed to people interacting and participating in various square sections, such as existing vegetation, shadows, porticos, and current land use. The survey was carried out based on the time the users spent in the space (Figure 4 and Figure 5). Table 6, Table 8, and Table 9 contain further information about the observation dates (activities observation hours /days), whereas Table 7 contains the user's survey-coded time in the square space.
Table 5. Frequency of observation (activities observation hours /days)
| Month | Monitoring days | Monitoring time | The number of monitoring hours | Type of activities | |
|---|---|---|---|---|---|
| from September 1 to December 1 | September 9/ Monday | Weekday | Day | 10‒2 pm | Informal group/individual activities |
| September 13/ Friday | Weekend | Day | 10‒2 pm | ||
| September 18/ Wednesday | Weekday | Day | 10‒2 pm | ||
| September 27/ Friday | Weekend | Day | 10‒2 pm | ||
| October 1/ Tuesday | Weekday | Day | 10‒2 pm | ||
| October 10/ Thursday | Weekday | Day | 10‒2 pm | ||
| October 18/ Friday | Weekend | Day | 10‒2 pm | ||
| October 23/ Wednesday | Weekday | Day | 10‒2 pm | ||
| September 12/ Thursday | Weekday | Night | 4‒8 pm | ||
| September 17/ Tuesday | Weekday | Night | 4‒8 pm | ||
| September 18/ Wednesday | Weekday | Night | 4‒8 pm | ||
| September 26/ Thursday | Weekday | Night and day | 10‒2 pm/4‒8 pm | ||
| October 1/ Tuesday | Weekday | Night and day | 10‒2 pm/4‒8 pm | ||
| October 7/ Monday | Weekday | Night and day | 10‒2 pm/4‒8 pm | ||
| October 18/ Friday | Weekend | Night and day | 10‒2 pm/4‒8 pm | ||
| October 21/ Monday | Weekday | Night and day | 10‒2 pm/4‒8 pm | ||
| October 22/ Tuesday | Weekday | Night and day | 10‒2 pm/4‒8 pm | ||
| October 25/ Friday | Weekend | Night | 4‒8 pm | ||
| October 28/ Monday | Weekday | Night | 4‒8 pm | ||
| October 29/ Tuesday | Weekday | Night | 4‒8 pm | ||
Table 6. Selected time for observation
| Time conditions | Selected times |
|---|---|
| Sunny, warm-season | Fall and early winter holidays (more time to hang out in public places) |
| ‘Time-out’ hours |
Weekdays: peak activities during lunch (10 am to 2 pm) and after work (4 pm to 8 pm); Weekend: (10 am to 8 pm); especially on Fridays when there are active retail activities. |
| Peak-hours | Morning (8 am-10 am); Afternoon (4 pm -8 pm): More people take buses or taxis, ride motorcycles or cars to and from work. |
|
• ‘Time-out’ hours are a period when people are going home for a short break (having lunch), and this square acts as a link between user’s origins and destinations, or, due to the mosque, people go to the mosque to pray at certain times during the week. • Peak hours is a period when people are going home after a long workday |
|
Figure 4. The dynamic monitoring of the Khan square- on Fridays when active retail activities
Figure 5. The static monitoring of the Khan square – Thursday: after Leaving Work
We then categorized all activities and interactions and then coded them to illustrate activity pattern graphs - the frequency of activity and the link between them (Figure 8, Figure 10, Table 8, and Table 9).
Table 7. Survey code (the time the user has been in the square space)
| Surveyed time interval | Between 1-4 minutes | Between 4-12 minutes | Between 12-20 minutes | More than 20 minutes |
|---|---|---|---|---|
| Code guide | 
|
|
|
|
Table 8. Classification of selected interactions
| Interact | |||||
|---|---|---|---|---|---|
|
|
|
Sudden |
1-4 minutes |
Inactive | Looking, hearing, seeing, being seen, going, coming. |
| 4-12 minutes | Active | Sitting, standing, talking, watching, running, children playing, smoking, conversation. | |||
| Routine | 12-20 minutes | Inactive | Walking, eating, drinking, listening, sunbathing, reading the newspaper, reading a book, parking. | ||
| More than 20 minutes | Active | Cleaning, meeting, sweeping, biking, exercising, taking a photo, sketching, playing music, shopping, selling. | |||
Table 9. Classification of selected human activities in a sustainable public space (urban square)
| Activity | |||||
|---|---|---|---|---|---|
|
|
|
Individual activities | 4-12 minutes |
|
Standing, sitting, looking, hearing, walking, sweeping, parking, smoking. |
| 12-20 minutes | Watching, running, eating, drinking, sunbathing, biking, listening, exercising, stepping. | ||||
| More than 20 minutes | Cleaning, reading a book, reading the newspaper. | ||||
| Group activities | 1-4 minutes |
|
Seeing, being seen, conversations, going, coming. | ||
| 12-20 minutes | Talking, shopping, selling. | ||||
| More than 20 minutes | Sketching, children playing, playing music, meeting, taking a photo. | ||||
Figure 6. Note taking and coding activity patterns in the Khan square - Types of activities
Figure 7. Note-taking and coding of activity pattern in the Khan square – Selected time
The qualitative study was influenced by geometric-physical aspects such as existing vegetation, tree shade, portico, land usage, and entrances. Apart from the six entrances, the red dashed line on the map below (Figure 8) illustrates the "shadow" as a sustainable environmental feature in the field area, effectively altering user activity/ movement and splitting the square into two equal sections for most of the day (See Figure 8 and Figure 9).
Figure 8. The Khan square entrances
Figure 9. Frequency of dominant trip types for entrances
We evaluated existing data on predominant trip generating types during morning and evening peak periods in the Khan square (Sevtsuk, Basu, Li, et al., 2021), which resulted in a selection of potentially relevant origin-destination combinations for our model, indicated in blue in Figure 10, Figure 11, and Table 10.
For entries 1, 2, and 3, the peak hours of the busiest weekday revealed the following:
For Fourth, fifth, and sixth entries:
The mapping technique helped us graph the activity patterns (the analysis index). Figure 10, Figure 11, and Table 10: The red nodes have grade four, the orange ones have grade three, and the black vertices have grades one and two. These degrees indicate the node's prominence in any urban space activity pattern graph. Thus, the greater the node's degree, the more users' options. We counted prominent trip kinds in the square space's outer layer (in areas with more shadows: the portico). The square's centre axis was the focus for users.
Figure 10. Khan square activity pattern map based on the frequency of dominant trip types
Figure 11. Khan square activity pattern graph
Indeed, the square's middle area was used for sunbathing, lounging, and relaxing. Nodes of grades three and four gave users more possibilities. Nodes with grades two and three also facilitated spatial-functional linkage, promoting social and dynamic activities. Individual activities become increasingly static and transient when the distance between individuals and gravity decreases. In addition to measuring environmental factors, user population size helps detect environmental traits and quality: The greater the centre geometric distance, the more active the activities. In the winter, people favour the sunniest portions of the plaza, whereas, in the summer, they prefer the shady parts. Sunlight, shadows, and the square's constraints influenced an activity since comfort enabled them to enjoy and spend more time there. The investigation found that the square's centre of gravity and the portico's axis produced informal individual and group activities for an extended period.
Table 10. Activity patterns graph analysis and its characteristics
| Vertices/ nodes | 39 | |
|---|---|---|
| Edges/ links | 46 | |
| G | 39, 46 | |
| V | P1, P2, P3, …, P39 | |
| E | V1, V2, V3, …, V46 | |
| G | deg(p1), (p6), (p8), (p9), (p29), (p30), (p31), (p34), (p39) | 1 |
| G | deg(p2), (p4), (p10), (p16), (p21), (p25), (p26), (p35), (p37) | 2 |
| G | deg(p3), (p67), (p11), (p13), (p14), (p15), (p17), (p18), (p19), (p20), (p22), (p24), (p33), (p36), (p38) | 3 |
| G | deg(p5), (p12), (p23), (p27), (p28), (p32) | 4 |
| δ(G) | 1 | |
| ∆(G) | 4 |
Figure 12. Frequency of human interaction at the thresholds, edges, paths, and nodes of Khan Square
Thresholds are viewed as spaces between public and private (Bobić, 2004; Hillier & Hanson, 1984; Norberg-Schulz, 1971; Stevens, 2006; Whyte, 1980). Simões Aelbrecht (2016) agreed that thresholds are critical behavioural limits, whether passive (e.g., water or green areas) or contained (e.g., sitting borders). Edge areas are frequently the ideal places for young people to test their social identity. Paths are important urban social hubs that provide human contact needs (Simões Aelbrecht, 2016). Nodes are strategic behaviour and entry/exit locations (Lynch, 1960). Using multiple dynamic nodes helps improve social integration. Unlike Lynch's nodes, Khan Square's nodes are not physically discernible. In fact, some of them are only peripheral. They only become nodes when used, or when events or situations occur (Simões Aelbrecht, 2016), see Figure 12.
Individual preferences, demographic biases (e.g., age, gender, and wealth influences), social norms (e.g., walking culture in a city or lack thereof), weather, time of day, and land-use interactions in the built environment shape pedestrian flows. A pedestrian flow model can forecast changes in activity due to changing land use, including updated residential, work, and amenity locations. We tried three questions in the subsections below (a) which pedestrian flow patterns dominate during rush hour? (b) Is the model's accuracy stable over time? Moreover, (c) the proportional significance of distinct pedestrian flows is consistent throughout time.
There are two types of network analysis indices for urban networks: inter-network indices and intra-network indices (Sevtsuk, 2014). Nodes can symbolize the repetition and diversity of activities in the social space (as determined by the user and unaffected by the physical space) and the edge, the path taken by the urban square's residents.
Inter-network Indices (Sevtsuk 2014)The gamma index quantifies how closely a geographic network resembles a fully connected diamond graph, in which every node is connected to every other node. The calculation is as follows (Sevtsuk, 2014):
Gamma index = e/ [(v2-v)/2] (eq 1)
Edges/ links: 46
Vertices/ nodes: 39
Gamma index = 46/ [(392-39)/2] = 1/16
where "e" denotes the number of edges, and "v" is the number of vertices on the graph. The higher the index, the greater the internal connectivity of the urban square and the more direct the movement of users between the square spaces, namely: the city square's integrity, inclusion, and assistance. The cyclomatic number forms another index that shows the availability of alternate routes between nodes in the network rather than unique routes. Specifically, the permeability, the publicity, and the activity incidence and social interactions are directly related to the square's size. Finally, involvement and trust increased, and the cycle index is defined as follows (Sevtsuk, 2014):
Cyclomatic number = e – v + g (eq 2)
Edges/ links: 46
Vertices/ nodes: 39
Cyclomatic number = 46-39+1 = 8
The maximum number of cycles for a given number of vertices is determined as the possible edges in a graph minus the edges in a tree graph with the same number of vertices and is calculated as follows (Sevtsuk, 2014):
Max. Cycles = [(v2-v)/2] - (v-1) (eq 3)
Edges/ links: 46
Vertices/ nodes: 39
Max. Cycles = [(392-39)/2] - (39 -1) = 703
They favour a hierarchical organizational pattern by restricting the number of possible movement pathways. Additionally, the more cycles on the graph, the more paths and possibilities are accessible in the square area. For example, in the Khan square, the shadow factor separates the square into two halves at certain times of the day, and the square's current circulation allows users to enter. It is inextricably linked to the square's equality and monitoring (surveillance).
The redundancy index indicates the network's susceptibility to split. The index is defined as the ratio of observed to maximum feasible cycles (Sevtsuk, 2014):
Redundancy index = (e – v + g) / [[(v2-v)/2] - v+1] (eq 4)
Edges/ links: 46
Vertices/ nodes: 39
Redundancy index = (46-39+1)/ [[(392-39)/2] - 39+1] = 1/88
When the redundancy index is 0 the network is one of several trees, when it is 1 the network is connected. The index may be used to examine the route network's volatility in a square space. Accordingly, the more the number tends to be 1, the denser and more consistent the graphs in the square activity pattern.
The square is more manageable. Finally, the square's qualities include maintenance, cleanliness, tidiness, and a sense of belonging.
This section illustrates the influence of inter-network indices on the Khan Square activity pattern graph. Substantially, the greater the number of network indicators, the higher the square's social quality.
Squares are critical for users to interact in vibrant, sustainable public areas. The activity will change based on the environmental capacity and factors. Furthermore, there is a clear correlation between human movement and other factors such as the core portion of the square, spatial depth, concentration, and time required; the more human movement, the greater the spatial depth, the more concentration. We provide new perspectives on methodologies and theories, expanding our understanding of sociality. By contrast, further study appears to be essential to undertake other case studies and research in various cultural and socioeconomic situations with diverse planning and design concepts.
This research might ultimately develop a design guideline for future urban expansions and projects. Guidelines based on configuration studies and human activity can influence urban development plans and design disciplines for new urban zones. Such data might assist planners, and transportation regulators in determining the most effective locations for new sidewalk enhancements, landscaping investments, street furniture installations, and safe crossing interventions.
Our model can forecast pedestrian flows on all sidewalks and crosswalks based on their location and time of day within a reasonable range of the calibration sites. Second, the proposed model can forecast the impact of the antiquity of existing buildings (place/space quality) on pedestrian traffic on adjacent streets. These may include actual development projects, new zoning proposals, or planning and urban design scenarios spanning parcels around the city. For instance, the model may be used in the early phases of neighbourhood redevelopment planning to analyse how alternative urban design schemes will likely affect future pedestrian traffic, identify where further public space investment is essential, and so forth.
Alternatively, it might be used to determine the impact of a proposed development on a particular lot—for instance, how a new shop would likely affect surrounding foot traffic. Such use-cases could be realized by hosting the calibrated model on an interactive, public web map that enables users to update land-use characteristics and built-density variables on individual lots interactively to assess the impact of proposed changes on foot traffic on nearby streets at various times of the day. The resultant Pedestrian Impact Assessment estimates may be used to collect financial commitments from developers to improve the most damaged pedestrian infrastructure—an approach that is currently standard for Traffic Impact Assessments.
Such data may aid in balancing policy and planning issues about pedestrians and automobiles. The model's unique features include the ability to estimate not only the changing volumes of walking trips associated with new urban developments but also their spatial distributions, as well as the ability to evaluate how specific urban development projects may affect, and thus benefit, foot traffic.
Our research, we think, will motivate more municipalities to use automated and anonymous pedestrian counting systems, resulting in more robust evidence-based planning for non-motorized journeys and space activities.
This research experienced several limitations due to its exclusive emphasis on one physical location as a sustainable public space. As a result, our study concentrated only on urban environments and user behaviour. Additionally, this research was limited to a particular scale of a public square, although various squares may be researched in several ways. For instance, square patterns in this study were limited to a single kind, which does not capture all square patterns. The sample size was limited to respondents who owned a car, motorcycle, bicycle, or were pedestrians; most respondents were residents. As a result, this study limited itself to design recommendations, nevertheless, more research on this criterion may form research guidelines for urban analysts.
