Did in-home activities fulfill activity needs during the COVID-19 pandemic?

Abstract

This study uses activity need to analyze the activity fulfillment of switching from out-of-home to in-home activities during the COVID-19 pandemic. Activity need is a measure to assess activity demand by determining how frequently people wish to engage in it. A panel time-use activity diary is used to collect daily activity in the Surakarta agglomeration area. Four-day activity diary was collected over one weekend day and three weekdays. This study analyzes the data from 402 residents using descriptive and inferential statistics. Exploratory factor analysis is used to identify the significant indicators of the built-environment variables. The activity attributes, sociodemographic characteristics, and the built environment are determined as the variables influencing activity needs. As a part of activity attributes, the activity types analyzed are working/studying, leisure, sports, and shopping. The activity needs that are unfulfilled by in-home activities include working/studying, sports, and leisure. The only activity need fulfilled by in-home activities is shopping. When analyzing sociodemographic variables, it was found that individuals with low income and older than 55 years have a greater risk of having unmet activity needs than those with the other indicators. Regarding the built environment, individuals living further away from the city center, administration office, junior/high school, hospital, and modern market have a higher level of unmet activity needs than those living in other residential environments.

Keywords:

• Activity attributes
• activity need
• built environment
• COVID-19 pandemic
• sociodemographic variables

REVIEWING EDITOR:

• Sanjay Kumar Shukla, Edith Cowan University, Australia

SUBJECTS:

• Planning
• urban studies
• urban policy

1. Introduction

Throughout history, pandemics have been acknowledged as a severe threat to public health, often resulting in widespread loss of life. Cholera, typhoid, malaria, smallpox, and influenza are among the diseases that have varying transmission methods and levels of severity; they can significantly impact human life and urban infrastructure. Given the recurring nature of pandemics, it is crucial to prepare for them and to promote the resilience of cities (Glaeser, 2020). The most recent pandemic was that of COVID-19, which started at the end of 2019.

The COVID-19 pandemic has caused a significant shock to cities worldwide due to its large impact on nearly all sectors. Currently, there are numerous academic and popular analyses attempting to answer two crucial questions: What is the impact of the pandemic on the city? Moreover, considering these changes, will they be long-lasting? Comparison research has identified the effects of several pandemic regulations on metropolitan cities in the USA (Vicino et al., 2022). The analysis of the effect on a city level has included analysis of the density, population trends, socioeconomic structure, transportation patterns, and economy; the effects vary based on the type of regional development patterns and the temporal mode of analysis (pre-, during, and post-pandemic). One study proposed an analytical framework that will serve as an analytical tool for future pandemic research and for determining how cities can become more resilient to such shocks (Vicino et al., 2022). Another study was conducted to determine how the COVID-19 pandemic has affected work, mobility, and housing in Toronto and whether it will impact the city’s growth. The study found that despite the pandemic, Toronto’s status as a second-tier global city remains a competitive advantage likely to persist in the future (Brail & Kleinman, 2022). Numerous studies have analyzed how the COVID-19 pandemic has affected urban structures, development, and resilience (Vicino et al., 2022; Brail & Kleinman, 2022; Florida et al., 2021; Batty, 2020), highlighting its significant and complex effects. Urban planners and engineers should consider these effects when creating policies.

From a different perspective, creating policies for a specific field, such as transportation, requires a thorough analysis. From 2019 to 2022, the COVID-19 pandemic had a global impact with a high number of fatalities (Hanifah & Siregar, 2022). The virus is easily transmitted and severely affects human health, often resulting in death. Many regulations to prevent transmissions have been implemented by governments worldwide, such as movement restrictions, closure of public facilities and unessential factories, and remote work, study, and shopping (Abdullah et al., 2020). Researchers have extensively studied the effects of the pandemic on the transportation sector. Government implementation of movement restrictions for all individuals, regardless of age, has affected various aspects of life, such as travel and activity behavior. Several travel behaviors have changed due to movement restrictions, including the frequency of trips, mode and route choices, and travel distances (Abdullah et al., 2020; Politis et al., 2021; Hook et al., 2021; Zhang, 2021). Consequently, many activities have shifted from out-of-home to in-home. For instance, people have adopted teleconferencing media, such as Zoom and Google Meet, to work and study from home using information and communication technology. Additionally, online shopping platforms, such as Lazada, Shopee, or Tokopedia, have enabled people to switch from offline to online shopping. As a result, people in Indonesia have significantly reduced their trips during the COVID-19 pandemic (Irawan et al., 2022). Changes in travel patterns in Greece have also led to a significant reduction in traffic volume, up to 50% during the pandemic (Politis et al., 2021). In Shanghai, the reduction was evident in the increased speed on city roads, which implies reduced traffic volume and congestion (Li et al., 2021).

Additionally, the COVID-19 pandemic has led to a positive change in transportation patterns. The movement restrictions and the shift of various activities from out-of-home to in-home have decreased travel and traffic volumes during the pandemic (Vicino et al., 2022; Politis et al., 2021). Hence, it is crucial to investigate which policies enforced during the COVID-19 pandemic can be continued to maintain the low traffic volume level even after it ends. In a study in the US, 50% of travelers in the US wanted to remain under a work-from-home policy even after the pandemic (Barbour et al., 2021). In conclusion, switching from out-of-home to in-home activity changes travel behavior. Therefore, it is essential to study this switch because when some activities are done at home, traffic volume is reduced.

Switching out-of-home to in-home activities influences the subjective and objective indicators of an individual’s resources and abilities and affects activity fulfillment. Each activity type has a specific purpose; thus, the variables of activity fulfillment are different among activity types. For example, in the activity of working/studying, achieving target performance is crucial. Switching from out-of-home to in-home working/studying may impact its performance. Work performance consists of many variables, including productivity and quality (Dinisari Hafat & Ali, 2022). In a previous study, the productivity of working from home was estimated based on subjective criteria using ‘worker self-evaluation’ and the reason for having a positive or negative experience. For another activity type, positive and negative aspects of online shopping were used to measure the fulfillment of online shopping activity (Shamshiripour et al., 2020). These analyses have limitations because they only utilize one or two predictor variables of activity fulfillment, among many others.

When analyzing the activity fulfillment for all activity types, it is required to analyze variables of activity fulfillment for all activity types. When this variable is designed based on a daily diary activity with an interval activity time duration of 15 min for 24 h, the questionnaire design becomes complex. In addition, a considerable effort is required to explain it to the respondents. Then, the appropriate approach to reduce the complexity is to measure the activity fulfillment not based on the activity fulfillment variables but on the individual assessment. From a terminology point of view, activity need is based on individuals’ assessments, and the activity fulfillment variables are determined implicitly from individual assessment for each activity type. Thus, it is important to measure activity needs to establish activity fulfillment due to switching activities from out-of-home to in-home.

Using activity need to measure activity fulfillment can be a limitation and a benefit. The benefit is that it can measure activity fulfillment for all activity types with a diary activity survey. The limitation is that there is no explanation of the activity fulfillment variables. To the best of the authors’ knowledge, using activity need to measure activity fulfillment due to switching from out-of-home to in-home activity has yet to be tested. Exploring the implementation of activity-need usage, this research measures activity fulfillment for the following activity types in detail: working/studying, sports, leisure, and shopping. To gain a thorough understanding of activity needs, this research also investigated how the value of activity needs varied based on sociodemographic characteristics and the built environment.

2. Literature review

Fulfilling needs is essential for maintaining overall well-being. Research that prioritizes needs can inform policy interventions aimed at ensuring that individuals’ needs are met (Nordbakke & Schwanen, 2015). Adopting a needs-focused approach to well-being allows policymakers to assess whether unmet needs exceed acceptable levels. The main idea behind need-based activity theory is that each activity serves one or more needs and people engage in activities to fulfill their needs; this is a concept that has been explored in various studies (Adler & Ben-Akiva, 1979; Axhausen, 2007; Arentze & Timmermans, 2009). The link between activities and needs is based on an individual’s assessment of the importance of participating in an activity and allocating time according to their beliefs. People have different opinions on which activities are more effective in fulfilling specific needs at any given time. As a result, some individuals may have unmet activity needs, while others may fulfill their needs, even if they spend the same amount of time engaging in the activities (Tonn, 1984). To measure activity need, researchers ask individuals if they would like to participate in an activity more frequently (Nordbakke & Schwanen, 2015; Scheiner, 2006; Kim et al., 2012).

The World Health Organization declared the coronavirus a global pandemic on March 11, 2020; the spread of the pandemic reached more than 114 countries with millions of cases (Matson et al., 2021). Countries affected by the pandemic have issued various lockdown policies, imposing stay-at-home orders and limiting nonessential travel. The implementation of lockdown regulations in countries worldwide has been proven to reduce the spread of COVID-19 (Rajabi et al., 2021); thus, such regulations were implemented to some extent during the pandemic.

The COVID-19 pandemic has caused people to shift their activities from out-of-home to in-home; therefore, some had to be changed to online activities (Muley et al., 2020). To engage in online education, work, and shopping, people require access to resources such as an internet connection (Yabe et al., 2021). The effect on these activities depends on various factors, such as the quality of internet access, the user’s technological proficiency, and the availability of support tools; these factors differ among individuals (Yabe et al., 2021). Additionally, the type of activity can also affect online engagement. Given the variation in performance and activity type, the fulfillment of online activities as in-home activities may also differ. Therefore, evaluating the fulfillment of activity needs when transitioning from out-of-home to in-home activities is appropriate.

Regarding activity type, some researchers discuss the transition from out-of-home to in-home activity for working, shopping, grocery shopping, and travel. One study measured the effect of switching from out-of-home to in-home working based on the individual perception of working productivity as it is believed that changing from offline to online work influences the work productivity of new and continuing telecommuters. The research found that 59% of new telecommuters reported no change or an increase in productivity. However, the remaining 41% had a negative experience. The significant representation of both groups in society highlights the need to investigate the underlying differences in more detail (Shamshiripour et al., 2020).

Numerous studies have been conducted on the impact of the COVID-19 pandemic on activity and travel behavior before, during, and after the pandemic. In the transportation approach, researchers investigated the changes in activity and travel patterns, such as trips, mode choice, activity type, and any other considerations (Abdullah et al., 2020; Aaditya & Rahul, 2021; Politis et al., 2021; Loa et al., 2023; Parker et al., 2021; Shin et al., 2022). Changes in mode choice occurred, public transport users were significantly reduced (e.g. to 80% in Hungary and 40–60% in Sweden), and individuals switched to private cars or bicycles (Politis et al., 2021). In a recent study, researchers analyzed the levels of travel and mobility across Europe, the USA, Africa, and Asia during the pandemic. The findings indicate a substantial decrease in travel, with Spain experiencing a 76% reduction in mobility, the USA a 60% reduction, and Hungary a 50% reduction. Additionally, the Netherlands and Italy saw a 55% and 50% decrease in total trips, respectively, and 41% of commuters in India stopped traveling altogether (Politis et al., 2021). These research results are evidence of the changes in travel behavior due to the COVID-19 pandemic. Changing travel behavior leads to reduced mobility and traffic volume. Switching from out-of-home to in-home activities is critical in reducing traffic volume (Politis et al., 2021; Irawan et al., 2022).

Numerous transportation studies have investigated how travel and activity behavior have shifted due to the COVID-19 pandemic, and how the COVID-19 pandemic has affected activity productivity. However, to the best of the authors’ knowledge, no study has yet explored the relationship between the change in activity types and one’s need fulfillment, which is a part of well-being. People engage in various activities to fulfill their needs, and changing from out-of-home to in-home activities may impact this. Therefore, a crucial question arises: do activities that are typically performed outside the home still meet people’s needs when they are performed inside the home?

When the need for some activities is fulfilled by an in-home activity, the government can support the implementation, such as provide better internet speed network and provide courses to increase human resources, especially in software skills, to reduce the traffic volume, which is expected to alleviate traffic congestion, pollution, and transportation expenses, ultimately supporting transportation sustainability. Working/studying is primarily considered because it generates more traffic volume than other activities (UK Government, 2013).

3. Materials and methods

This research was conducted using quantitative analysis to identify the variations in and describe the characteristics of activity needs based on sociodemographic characteristics and built-environment conditions. A household interview survey was conducted to collect diary activity including the level of activity need for the activity type.

The previous analysis of activity needs was focused on older people since it was conducted under normal conditions, and they are expected to have difficulties in travel activities. Commonly, older people have problems with poor health and well-being conditions, poor access to transport resources (access to a car in the household and availability and quality of public transport), and gender disparities (Nordbakke & Schwanen, 2015; Luiu & Tight, 2020). However, this research was conducted during the COVID-19 pandemic, which enforced movement restrictions and social distancing for everyone; thus, examining activity needs across activities and ages was important.

3.1. Sample

The study investigated the population older than 17 years in the Surakarta agglomeration area. The total area of Surakarta Municipality is 46.72 km². It is the smallest among the surrounding areas, as shown in Figure 1. In 2021, the total population of Surakarta was 522,728, with a population density of 11,187.52 residents/km² and a growth rate of 0.07% from 2020 to 2021. The gross regional domestic product in Surakarta Municipality increased gradually from 2017 to 2021, and the value in 2021 was Rp 36,211,250,000 (Surakarta, 2022). These conditions support the development of the surrounding cities so that they become connected with Surakarta. Therefore, the city of Surakarta can be considered the agglomeration area of Surakarta according to the definition of an agglomeration city, which is an interconnected area that can be united into one place although it is different administratively. Agglomeration can also be interpreted as a territorial unit consisting of several interconnected city centers and districts.

Figure 1. Location of the study.

Documentation of daily activities during the COVID-19 pandemic took place over four days, encompassing one weekend day; Sunday, and three weekdays; Monday, Tuesday, and Wednesday. The data were collected from March 27 to June 28, 2021, which coincided with the implementation of Pemberlakuan Pembatasan Kegiatan Masyarakat ber-skala Mikro (PPKM Mikro) regulation based on the 2021 Instructions of the Minister of Home Affairs No. 06 to No. 13. The PPKM Mikro regulation established command posts to handle the COVID-19 pandemic at the smallest level units in cities/districts and villages, as the first peak of cases occurred on July 17, 2021. The government imposed specific provisions for various activities under PPKM Mikro. Offices, restaurants, and places of worship were allowed to operate at a maximum of 50% capacity, while malls and construction centers could operate until 21:00, and public facilities and sociocultural activities remained closed. However, essential sectors related to basic needs, such as construction, were allowed to operate at full capacity with strict health protocols in place. These measures aimed to prevent a complete halt to economic activity.

The inhabitants of the Surakarta agglomeration area were unfamiliar with the terms ‘activity needs’ and ‘activity type’. Furthermore, the questionnaire had many questions, so some responses were condensed into a table. Surveyors had to interact face-to-face with the respondents to ensure they understood the phrases and how to complete the questionnaire because the inhabitants were unfamiliar with teleconference technology and usage. Additionally, the research population was quite extensive. To save time and resources, convenience sampling, a type of nonprobability sampling, was utilized. To conduct the sampling, researchers selected ten undergraduate students to act as surveyors. These students began by surveying their own families before moving on to their neighbors’ and friends’ families. They only surveyed families who lived in the Surakarta agglomeration area.

The sociodemographic data from 402 respondents were collected. The profile of respondents is shown in Table 1.

Table 1. Sociodemographic profile.

Variable	Frequency (Percentage)
Sociodemographic characteristics
Gender
Male	200 (49.75%)
Female	202 (50.25%)
Age
18 - 22 years	117 (29.10%)
23 - 45 years	109 (27.11%)
46 - 55 years	110 (27.36%)
Above 55 years	66 (16.42%)
Occupation
Nonworker	82 (20.40%)
Worker	203 (50.50%)
Student	117 (29.10%)
Income
Low income (< 3 million IDR/month)	145 (37.25%)
Medium income (3–6 million IDR/month)	121 (29.41%)
High income (> 6 million IDR/month)	136 (33.33%)

3.2. Measures

The measured variables include daily activity and activity needs, travel patterns, sociodemographic characteristics, and the built environment. The activity diary survey documented all activity types with activity time duration with intervals every 15 min for 24 h a day, resulting in 96 time windows in one day. This approach minimized bias in determining the time spent on various activities and was simple for the surveyors and respondents to execute. However, it is essential to recognize that this method could not track activities shorter than 15 min or travel time less than 15 min (Dharmowijoyo et al., 2015). The categories of activity types in this research include both in-home and out-of-home activities and mandatory and discretionary ones; this categorization refers to a study conducted in the Bandung Metropolitan Area (Dharmowijoyo et al., 2015). The built-environment indicators include distance to public facilities and the quality of the residential environment. Travel patterns were measured by number of trips, trip chains, and travel time. Sociodemographic characteristics included gender, age, occupation, income, housing type, and property rights. The level of activity need and the quality of the residential environment were measured using a Likert scale.

Previous measuring of activity needs determined whether participants faced difficulties and barriers when undertaking out-of-home activities. Measuring the difficulties in engaging in out-of-home activities can be approached by measuring subjective indicators and asking for the individual’s perception. The perception can be expressed based on problems, for example, with transportation, by responding to, ‘Did you have any transportation difficulties in any of these journeys?’ (Luiu & Tight, 2020). This approach is appropriate under normal conditions. However, during the COVID-19 pandemic, the implementation of movement restrictions was the problem; thus, the perception of an individual that needs to be analyzed does not refer to any facilities, only whether the activity need of the individual can be fulfilled by an in-home activity. As in a study in Norway (Nordbakke & Schwanen, 2015), the individual perception can be explored by asking, ‘Would you like to participate more frequently in these activities?’ When the individual answers that they would like to participate more frequently, it means that the individual has an unmet activity need. The responses are recorded on a seven-point Likert scale.

The Likert scale is used to measure opinions or attitudes. It is considered an ordinal scale; that is, the responses are ranked in order. A good Likert scale can be transformed from an ordinal to an interval scale. The resulting interval-scale data can then be analyzed with various statistical methods. When transforming the Likert scale from an ordinal to an interval scale, it is necessary to use a minimum of seven levels (Hair et al., 2017; Hair et al., 2019). Thus, this research used a seven-point Likert scale.

Many previous research results state that land use, such as high-density and mixed-use neighborhoods, is associated with travel behavior, such as mode choice and travel time (Fan, 2007; Liu & Silva, 2017; Næss, 2005). However, a consensus on the strength of this relationship has not been reached. Confounding findings have been obtained because of different research designs as well as a variety of geographical contexts in which previous studies were performed (Van Acker et al., 2007). A new approach to density explores density as a complex set of socio-spatial relationships in which topology, scale, and socio-spatial markers matter, including the distribution of class, race, gender, and colonial dependency (Keil, 2020). In the case of the Surakarta agglomeration area, the race is dominated by Javanese, so this new approach is not implemented within this research. Despite the extended literature on the effect of land use on travel behavior, a threefold distinction concerning variables that influence travel behavior can be found. These dimensions are based on the type of variables, including the spatial, socioeconomic, and personality dimensions (Næss, 2005; Van Acker & Witlox, 2005). Many previous studies correlated these three dimensions with the variable of travel behavior, such as trip, mode choice, and travel time, whereas this research analyzes the correlation with activity systems. The activity system approach offers a framework within which daily activity and travel can be analyzed. Living, working, shopping, and recreation are spatially separated activities, inducing the need to travel (Van Acker et al., 2007). In this research, the activity systems were approached through activity need. Furthermore, the explored spatial dimensions were the distance from the public facilities and the residential environment; these variables refer to the study of Luiu in 2021 that resulted in the two indicators of the built environment that influence travel difficulties: regional location and context of the residential area (Luiu & Tight, 2020). Table 2 shows the essential variables, measurement, and basic statistical analysis used.

Table 2. Variable measurements.

Variable	Measures	Response	Basic Statistic
Activity Need to do:	Would you like to participate more frequently in these activities?	Likert Scale: Very needed Needed Fairly needed Not needed and nonessential Fairly nonessential Nonessential Very nonessential	Mean, Figure 8.
In-home Mandatory (IhMo)
In-home Maintenance (IhMT)
In-home Leisure (IhLe)
In-home Online Activity (IhOa)
In-home Working Studying (IhWoSy)
In-home Sport (IhSpt)
Out-of-home Online Rest (OhOna)
Out-of-home Offline Rest (OhOfa)
Out-of-home Socializing/recreation (OhSoRe)
Out-of-home Household Act (OhHa)
Out-of-home Baby Sitting (OhBs)
Out-of-home Working and Studying (OhWoSy)
Out-of-home Grocery Shopping (OhGs)
Online Shopping (OnSh)
In-store Shopping (InSh)
Out-of-home Sport (OhSpt)
Out-of-home Maintenance (OhOM)
Out-of-home Picking up and Dropping off Children (OhPc)
Built Environment:
Distance to public facilities:	How many kilometers is the distance between your residence and the ……….?	Fill in: …. km	Mean, Table 3 EFA, Table 4 Bivariate analysis, Figure 5
City center
Bank/post office
Pre-school
Kindergarten
Elementary school
Junior/senior high school
Hospital
Station
Bus stop
Supermarket
Traditional market
City Park
Government office
Shopping center
Clinic/health center
Residential environment:	Do you think that your neighborhood is/has …….?	Likert scale: Strongly disagree Disagree Neutral Agree Strongly agree	EFA, Table 5 Bivariate analysis, Figure 6
Comfortable place to stay
Good infrastructure
Clean environment
Safe area
Drug abuse
Destruction (graffiti)
Annoying youth
Beautiful place
Peaceful place
Safe for kids
Friendly neighbors
Connected to public transport
Sufficient parking area
Good facilities
Good health facilities
Big supermarket
Good traditional market
Good school standards
Rare traffic jams
Safe traffic
Lively
Happy atmosphere
Frequent communication with neighbors
Familiar with the environment
Citizen meeting
Frequent meetings to attend
Sociodemographic
Housing Type:	What type of residence do you live in?	Fill in:	Categorical, Figure 3
A. Permanent A House with 1 to 2 floors for the upper class, with suitable construction materials, garages for more than 2 cars, and a large park		A
B. Permanent B 1- to 2-floor house, 100–200 m^2, for the middle to upper class, with suitable construction materials, garage for 1–2 cars, with a sufficiently large park		B
C. Permanent C House with 1 floor, 45–100 m^2, for the middle class with with or without a garage and a small garden		C
D. Permanent D Simple house, 21–44 m^2, for the lower middle class, with or without small garden		D
E. Semi-permanent house with garden		E
F. Temporary house without garden		F
G. Others		G
Property Right:	Which describes your living status?	Fill in:	Categorical, Figure 4
A. Own		A
B. Buy		B
C. Rent (annual)		C
D. Rent (monthly)		D
E. Official residence		E
F. Occupy without a rental fee		F
G. Other		G
Travel pattern
Travel time percentage of using mode:	Travel time data were collected alongside activity type using an activity diary, with 15-min intervals for 24 h a day. For the travel mode, the question was: "When you travel to the activity, which travel mode to you use?"	Fill in:	Bivariate analysis, Figure 9
Nonmotorized		Nonmotorized
Motorcycle		Motorcycle
Car		Car
Public Transport		Public Transport
Ride-sourcing		Ride-sourcing

3.3. Data analysis

Descriptive statistics were used for sociodemographic characteristics. Exploratory factor analysis was applied to the built environment to define the indicators of distance to public facilities and residential environments. Additionally, bivariate analysis was executed for the variation analysis of activity needs based on the built environment and travel pattern, especially mode choice.

4. Results

4.1. Sociodemographic characteristics

When analyzing sociodemographic characteristics as endogenous variables, the correctness of the construct variable must be confirmed. The variable consists of a number of indicators, which should differ significantly. An analysis of variance was used to determine the existence of significant differences among indicators. Figures 2, 3, and 4 display the average activity need based on the sociodemographic indicators.

Figure 2. Activity-need level based on sociodemographic characteristics.

Figure 3. Activity-need level based on housing type.

Figure 4. Activity-need level based on property right.

In Figure 2, the average level of activity need based on sociodemographic factors, such as income, status, age, and gender are shown. The mean activity need falls between 5.00 to 5.40. Interestingly, individuals who are 55 years old or older have the highest activity-need level. In terms of income, those belonging to the low-income group require the highest level of activity. This finding is consistent with the results for housing type and property rights shown in Figures 3 and 4. Specifically, the ‘Other’ for housing types and ‘monthly rentals’ for property rights are associated with lower income levels.

4.2. Built-Environment analysis results

In the Surakarta agglomeration area, there are many public facilities that the community can access to meet their needs. Many activities that often cannot be done at home include studying, working, maintaining health, shopping for groceries, and recreation. This study collected distances from the participants’ homes to 15 types of public facilities, as shown in Table 3.

Table 3. Distance to public facilities.

Variable	Distance (km)
	Mean	Standard Deviation
Built Environment: distance to
city center	7.78	8.89
bank/post office	3.95	7.99
pre-school	0.91	0.85
kindergarten	1.22	1.67
elementary school	1.14	1.28
junior/senior high school	1.79	1.79
hospital	3.00	3.54
station	5.21	5.19
bus stop	1.81	2.32
supermarket	2.86	3.43
traditional market	1.64	1.25
city park	4.21	4.37
government office	3.97	4.82
shopping center	5.08	6.01
clinic/health center	1.50	1.16

However, to construct the built-environment variable the number of indicators should be reduced from 15. To reduce the number, exploratory factor analysis (EFA) was used.

The result of the EFA analysis is the following three significant indicators (Table 4):

Table 4. Loading factors of distance from public facilities.

	Residing far from the city center, administration office, junior/high school, hospital, and modern market	Residing far from public transport, traditional market, city park, and clinic/health center	Residing far from primary school
City center	0.809	0.227	0.090
Bank/post office	0.906	0.016	0.118
Pre-school	0.135	0.093	0.894
Kindergarten	−0.074	0.194	0.754
Elementary school	0.251	−0.121	0.814
Junior/senior high school	0.728	0.023	0.437
Hospital	0.828	0.273	0.098
Station	0.416	0.721	0.168
Bus stop	0.046	0.837	0.010
Supermarket	0.670	0.497	−0.057
Traditional market	0.086	0.775	0.055
City Park	0.549	0.562	−0.103
Government office	0.638	0.175	0.108
Shopping center	0.666	0.518	−0.070
Clinic/health center	0.199	0.442	0.102
KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.797
Bartlett’s Test of Sphericity	Approx. Chi-Square	4173.231
	df	105
	Sig.	<0.0001

Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 6 iterations.

1. Residing far from the city center, administration office, junior/high school, hospital, and modern market

2. Residing far from public transport, traditional market, city park, and clinic/health center

3. Residing far from primary school

These three indicators are the significant indicators that construct the variable distance to public facilities. The next step in the bivariate analysis involves determining Z-scores to classify the distances as close or far, followed by data aggregation. The analysis results can be seen in Figure 5.

Figure 5. Activity-need level based on distance to public facilities.

The level of activity need varied among different groups, with values ranging from 5.04 to 5.30. People who live far away from the city center, administration office, junior/high school, hospital, and modern market have a higher activity need than the others. Conversely, those who reside far from public transport, traditional markets, city parks, and clinic/health center have a lower activity need. Figure 5 depicts the outcomes, and urban/transportation planners can use this information in combination with other analysis results to consider land use policies regarding the distance between public facilities and residential areas.

Another endogenous variable of the built environment is the residential environment. A total of 26 indicators (Table 5) were tested in this study. The results of factor analysis concluded that three indicators had no significant correlation with the residential environment because the load factor was less than 0.5. These were the following: safe for kids, have good health facilities, and happy atmosphere (Table 5). Moreover, the analysis yielded seven significant indicators as follows:

Table 5. Loading factors of the residential environment.

Rotated Component Matrix^a
	Indicator
	Good facilities; comfortable, beautiful, and peaceful	Good neighborhood environment	Safe environment and friendly neighbors;	Good public facilities (supermarkets, traditional markets, and schools)	Safe traffic and rare traffic jams	No Drug abuse, Destruction (graffiti), and annoying youth	Connected to public transport
Comfortable place to stay	0.607	0.336	0.262	0.176	0.215	0.048	−0.221
Good infrastructure	0.694	0.372	−0.065	−0.139	0.142	0.268	0.239
Clean environment	0.777	0.184	0.205	−0.016	0.075	0.103	0.069
Safe area	0.354	−0.021	0.561	−0.138	0.231	0.283	0.190
No drug abuse	0.326	0.030	−0.059	−0.119	0.228	0.536	−0.456
No destruction (graffiti)	−0.030	0.000	0.139	0.077	−0.026	0.857	0.011
No annoying youth	0.091	−0.059	0.198	0.016	−0.185	0.793	0.109
Beautiful place	0.633	0.157	0.354	0.247	0.071	−0.035	0.109
Peaceful place	0.494	0.004	0.421	0.344	0.282	0.107	−0.143
Safe for kids	0.399	−0.060	0.340	0.370	0.489	−0.065	0.061
Friendly neighbors	0.185	0.218	0.822	0.018	−0.007	0.151	0.049
Connected to public transport	0.225	0.045	0.107	−0.011	−0.006	0.092	0.837
Sufficient parking area	0.640	0.096	0.174	0.297	−0.126	−0.119	0.066
Good facilities	0.596	0.232	0.059	0.260	0.143	0.092	0.281
Good health facilities	0.139	0.347	0.004	0.441	0.303	0.201	0.303
Big supermarket	0.155	0.110	−0.072	0.795	−0.116	0.220	−0.042
Good traditional market	0.107	0.146	0.165	0.795	0.190	−0.133	−0.021
Good school standards	0.204	0.140	−0.007	0.512	0.299	−0.228	0.397
Rare traffic jams	0.030	0.076	0.038	−0.027	0.858	−0.052	−0.133
Safe traffic	0.117	0.142	0.244	0.205	0.696	−0.088	0.178
Lively	0.226	0.382	0.596	0.141	0.272	0.138	0.043
Happy atmosphere	0.294	0.483	0.476	0.120	0.300	0.094	0.018
Frequent communication with neighbors	0.280	0.670	0.191	0.043	0.221	−0.108	0.014
Familiar with the environment	0.154	0.803	0.258	0.077	−0.002	−0.191	−0.053
Citizen meeting	0.077	0.853	0.050	0.102	−0.121	0.012	0.060
Frequent meetings to attend	0.175	0.762	−0.006	0.157	0.152	0.123	0.081
KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy.				0.793
Bartlett’s Test of Sphericity		Approx. Chi-Square		5715.882
		df		325
		Sig.		<0.0001

Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 8 iterations.

1. Good facilities; comfortable, beautiful, and peaceful;

2. Good neighborhood environment;

3. Safe environment and friendly neighbors;

4. Good public facilities (supermarkets, traditional markets, and schools);

5. Safe traffic and rare traffic jams;

6. No drug abuse, destruction (graffiti), and annoying youth;

7. Connected to public transport

From the results of the EFA, it can be concluded that the residential environment variable is constructed using seven indicators. The next step was determining the Z-scores and conducting an aggregation analysis. The results are shown in Figure 6.

Figure 6. Activity-need level based on residential environment.

People living in areas with good public facilities, such as supermarkets, traditional markets, and schools, have a higher value of activity needs. By contrast, those residing in areas with safe traffic and minimal traffic congestion have a lower value of activity need. Additional findings are presented in Figure 6. The analysis result of the residential environment can be compared to the analysis result of the distance to public facilities. The comparison results must be logically valid to become an essential consideration for urban and transportation planners to create policies.

4.3. Activity attributes

This study assessed various indicators to analyze activity attributes, including but not limited to the daily time allocation, number of trips, trip sequence, travel time, and time allocation for each activity. Additionally, the study calculated the level of activity need for all activity types, each specific type, and the travel time percentage using the mode.

Table 6 displays how people spend their time on different days. The time measurement unit is minutes. Some activities have a moderate coefficient of variation, while others have a high one.

Table 6. Daily time allocation in minutes.

Activity Type	Sunday		Monday		Tuesday		Wednesday
	Mean	Standard Deviation	Mean	Standard Deviation	Mean	Standard Deviation	Mean	Standard Deviation
In-home Mandatory (IhMo)	613.5	151.8	590.6	121.6	596.7	126.5	585.5	123.5
In-home Maintenance (IhMT)	145.6	102.9	149.7	110.6	144.7	110.3	147.9	114.8
In-home Leisure (IhLe)	273.5	180.2	230.9	141.5	226.4	137.0	232.3	135.2
In-home Online Activity (IhOa)	191.8	157.8	166.5	150.7	159.1	146.3	159.3	144.6
In-home Working Studying (IhWoSy)	233.4	174.8	309.5	169.1	308.2	162.6	307.4	153.8
In-home Sport (IhSpt)	85.0	46.9	65.3	34.7	68.9	33.7	64.9	34.9
Out-of-home Online Rest (OhOna)	149.7	134.1	76.5	54.5	80.1	70.4	78.1	61.5
Out-of-home Offline Rest (OhOfa)	60.6	63.9	65.6	52.5	51.7	33.5	47.4	32.8
Out-of-home Socializing/recreation (OhSoRe)	185.4	125.8	115.1	108.0	118.8	99.4	114.9	96.8
Out-of-home Household Act (OhHa)	110.5	89.4	52.5	22.5	66.0	36.2	56.3	22.2
Out-of-home Baby Sitting (OhBs)	90.0	45.0	350.0	217.8	450.0	45.0	232.5	188.7
Out-of-home Working and Studying (OhWoSy)	339.6	175.5	372.2	139.2	372.7	135.5	369.5	149.7
Out-of-home Grocery Shopping (OhGs)	75.0	52.9	52.2	28.4	47.4	24.5	57.3	41.0
Online Shopping (OnSh)	47.5	20.2	52.5	34.4	40.0	25.5	45.0	9.5
In-store Shopping (InSh)	87.1	62.4	75.0	46.8	85.0	7.1	121.3	62.0
Out-of-home Sport (OhSpt)	131.3	76.2	111.7	56.6	107.5	43.7	124.6	80.5
Out-of-home Maintenance (OhOM)	71.7	54.0	46.7	31.6	45.0	29.0	58.6	49.0
Out-of-home Picking up and Dropping off Children (OhPc)	90.0	30.0	66.8	32.2	77.5	46.2	58.5	41.1

Interestingly, the duration of activities varies across the days. Overall, in-home mandatory activities took the longest time within a day. Compared with the other days, the in-home mandatory activities took the longest time on Sundays, with an average duration of 613.5 min. For out-of-home mandatory activities, the duration was shorter on Sundays than on weekdays. By contrast, it was the opposite for discretionary activities performed outside of the home. The activity-need analysis within this research focused on weekdays.

Table 7 displays the average number of trips and trip chains per respondent per day, along with the percentage of travel time for each mode of travel. Private vehicles, particularly private cars, were the most commonly used modes of transportation.

Table 7. Travel patterns.

Variable	Percentage or mean
Individual travel characteristics
Trip (/respondent/day)
number of trips	1.87
number of trip chains	0.88
Travel time (min/respondent/day)	49.07
Travel time percentage of using mode
nonmotorized	22.43%
private car	55.51%
motorcycle	19.77%
ride-sourcing	0.46%
public transport	1.83%

The composition of activity-need level for all activity types, in-home and out-of-home activities, as well as mandatory and discretionary activities, is shown in Figure 7. The modus class is in the ‘fairly needed’ category at level 5 on the Likert scale. During the COVID-19 pandemic, individuals’ perception states that 70.8% of activity-need levels were ‘fairly needed’ or above. These data were used to verify the average activity-need level for each activity type in Figure 8; generally, the average activity-need level for each type is near 5.

Figure 7. Distribution of activity-need levels across all activity types.

Figure 8. Bivariate analysis of activity-need level based on activity types.

Tables 6 and 7 display the activity patterns at the study location. When integrated with the activity-need values (Figure 8), the data can support arguments for activity fulfillment analysis.

In Figure 8, the average activity need for 19 activity types is displayed. This research focuses only on working/studying, sports, leisure, and shopping activities. Out-of-home shopping is called in-store shopping, and out-of-home leisure is called out-of-home socializing and recreation. To analyze activity fulfillment, the activity-need values for activities done at home versus activities done outside the home are compared, considering activity attributes, sociodemographic variables, and built-environment data.

This study examined the connection between transportation modes and different types of activities. The results showed that people who drive cars for extended periods want to increase their out-of-home grocery shopping, with an average Likert scale score of 6.4. By contrast, the lowest activity need was found for working/studying. Those who use public transportation longer tend to rely on it for traveling to working/studying. Any other information relating to the use percentages of various modes and activity types is displayed in Figure 9. This information can help determine the necessary modes of transportation and the activity types during a pandemic.

Figure 9. Bivariate analysis of activity-need level based on use percentages of modes.

5. Discussion

During normal conditions, researchers estimate individual mobility based on the individual need to engage in out-of-home activities. Higher need values of out-of-home activities indicate higher levels of unmet mobility (Nordbakke & Schwanen, 2015). This study applied this approach to determine fulfilled or unfulfilled activity needs due to the shift from out-of-home to in-home activities during the COVID-19 pandemic; the data collection coincided with government regulation implementation to reduce the virus spread. For a specific type of activity, if the activity-need value for the out-of-home activity is higher than that for the in-home activity, it can be concluded that the activity need is not fulfilled by the in-home activity.

Researchers agree that both objective and subjective indicators of an individual’s resources and abilities influence their unmet needs (Nordbakke & Schwanen, 2015; Luiu & Tight, 2020). To measure the fulfillment of out-of-home activity to estimate mobility, a study used mobility and contextual conditions as indicators of resources and ability (Nordbakke & Schwanen, 2015). The research approach during the COVID-19 pandemic situation was to analyze the activity fulfillment of working as the effect of changing activity from out-of-home to in-home using the working productivity indicator (Shamshiripour et al., 2020). To complement those results, in this study, the switch from out-of-home to in-home activity was measured subjectively through individual perceptions of activity needs. The study configured individual resources based on variables, such as household income, housing type, and property rights; however, it did not consider individual abilities.

To contribute to the literature, this study analyzed the relationship between significant indicators of endogenous variables, including sociodemographic characteristics, built environment, and travel patterns, and activity needs. These significant indicators provided initial data for the subsequent research to analyze how endogenous variables affect activity need. Additionally, this study examined the variation in activity need using these endogenous variables.

Human needs and desires are related to socioeconomic and cultural factors. Income is an important but not the only determinant (Fan, 2007; Shen et al., 2015). According to the statistical analysis results in this study, the activity-need value varied significantly with gender, age, occupation, and household income. Researchers can use the classification of each predicted variable to analyze the effect of sociodemographic characteristics, as endogenous variables, on activity needs. Based on Figure 2, the level of activity need is high in male, people aged above 55 years, workers, and those with low income. The highest value is for those above 55 years old, followed by those with low and middle incomes. It can be stated that age and income are the significant sociodemographic factors that influence activity needs. These results support those of previous research, which found that older adults have more unmet activity needs than younger adults (Nordbakke & Schwanen, 2015; Luiu & Tight, 2020; Nordbakke & Schwanen, 2014).

Other relevant sociodemographic factors include housing type and property rights (Boarnet & Crane, 2001). This research analyzed the variation of activity-need levels based on these factors. Figures 3 and 4 show that households living in ‘other’ housing types, and ‘monthly rental’ property have the highest unmet activity need indicating a lack of resources and correlating with the lowest income groups. These results are consistent with the variation in activity need based on income.

In the built environment, the EFA yielded three indicators of distance to public facilities that vary by activity need (Table 4). Figure 5 displays that communities with greater distances from the city center, administration office, junior/high school, hospital, and modern market have the highest activity needs. These results imply that the government should provide these public facilities near residential areas.

Figure 6 shows the variety in activity needs based on the residential environment; from the 26 indicators tested in this study, seven significant components of indicators were found. The community living in a residential area with good public facilities (supermarkets, traditional markets, and schools) had the highest activity need, which means that the community wants to do the activities more frequently than the others. In other words, the community living near good public facilities (supermarkets, traditional markets, and schools) has more unmet activity needs than those living in different residential environments. An environment with good public facilities usually has a high land price; therefore, only people with high incomes can live there. Maslow’s theory presents this hierarchy of needs in a pyramid shape, with basic needs at the bottom and more high-level self-actualization needs (intangible needs) at the top. A person can only move on to addressing the higher-level needs when their basic needs are adequately fulfilled. Basic needs relate to physical needs, and basic physical needs include clothing, food, and shelter. For people with high incomes, the basic needs are fulfilled; therefore, the community in this residential environment has higher-level needs (Miller, 2017). To fulfill the higher-level need, the number of activities and activity types is higher while the time budget, at aggregate level spending, is nearly the same as that of the others. Therefore, the activity need is high because the community has more activity needs to fulfill. The second residential area with high activity needs is the one connected to public transport. This is because these communities rely on public transportation for their out-of-home activities, which can take longer due to the slower average travel speed of public transit in the Surakarta agglomeration area; however, the time budget to do activities for a day for these communities is similar to that of the others. Analyzing the distance to public facilities aligns with this analysis of residential environments. The community living far from public transportation has the highest activity needs fulfilment. The government should improve public transport performance to improve the fulfillment of activity needs.

For travel behavior analysis, comparing the normal and COVID-19 pandemic conditions, the number of trips and trip chains during the pandemic was lower. This survey was conducted in 2021 under movement restrictions implemented by the government to reduce the spread of COVID-19. The average number of trips of the 402 respondents was 1.87/day, and the average number of trip chains was 0.88/day. In the 2013 Bandung Metropolitan Area survey, which was conducted during normal conditions, the number of daily trips was 2.64 on weekdays and 2.29 on the weekends. In addition, the number of trip chains per day was 1.26 on weekdays and 1.08 on weekends (Dharmowijoyo et al., 2016; Syahputri et al., 2020). The number of trips and trip chains in the Surakarta agglomeration area during the COVID-19 pandemic in 2021 was lower than the number of trips and trip chains in the Bandung Metropolitan Area in 2013 under normal conditions. An online survey conducted in 2020 found that people in Indonesia significantly reduced their trips and trip chains during the COVID-19 pandemic, with a reduction of 71.28% and 15.82%, respectively (Irawan et al., 2022). Therefore, it can be concluded that the results of this research support those of the previous research.

The average time allocation of 18 activities is displayed in Table 6. The most extended time was allocated for in-home mandatory (IhMo) activity, and the lowest for Online Shopping (OnSh). However, in movement restriction implementation, the time allocation percentage of out-of-home working/studying was higher than that of in-home working/studying. Despite out-of-home working/studying having a longer duration, individuals had a higher activity need for it than for in-home working/studying, as shown in Figure 8. This means that individuals need to work/study out-of-home more often, as the in-home activity does not meet their activity needs.

A study in the US in 2020 found that nearly 50% of respondents who did not work from home before but started to do so during the COVID-19 pandemic expressed a willingness to continue working from home (Barbour et al., 2021). In our study, the activity need for OhWoSy was 5.22, whereas that for IhWoSy was 4.94. The measuring instrument used in this study differs from that of the US study, making direct comparison inappropriate. However, given the slight difference in activity-need value, it can be estimated that these two study results are consistent. Another study conducted in Chicago in 2020 found that, for working activity, 59% of new telecommuters reported no change or increase in working productivity. However, the remaining 41% had a negative experience (Shamshiripour et al., 2020). Although working from home can significantly alleviate traffic congestion and air pollution if adopted by a large portion of workers, the government should study in detail the subjective and objective indicators of individual resources and ability so that the implementation of working from home fulfills the work productivity and quality.

Amidst the pandemic, numerous studies have been conducted to explore the correlation between leisure activities and health and well-being. All studies have indicated a positive impact of leisure on health and well-being, with various factors affecting the outcome (Kim & Kim, 2022; Marques & Giolo, 2020; Li et al., 2022). A previous study examined the benefits of in-home and out-of-home sports. Participants in both categories adopted health prevention behaviors during COVID-19 but faced interpersonal constraints (Li et al., 2022). The authors recommended further research to compare the effects of in-home and out-of-home sports and leisure activities. According to Figure 9, the need for out-of-home sports and leisure activities is higher than that for in-home ones, indicating that individuals need to engage in out-of-home activities more frequently. Therefore, in the case of future pandemics, personal and institutional efforts should be made to promote participation in out-of-home leisure sports to fulfill the population’s needs.

The activity need for online shopping was 5.05, out-of-home grocery shopping was 5.06, and in-store shopping was 4.89. By comparison, a Chicago metropolitan area study in 2020 concluded that a significant portion of society still relies on in-store shopping as their primary choice in meeting grocery needs (Shamshiripour et al., 2020); therefore, the results of these two studies are aligned as online shopping has a higher activity need than in-store shopping, with only a slight difference in this study.

Additionally, during the COVID-19 pandemic, most individuals had a higher need for out-of-home socializing than that for other activities (Figure 9). This is very reasonable as, because of the social-distancing implementation regulations, nearly all activities were in-home. Table 7 displays that the average daily travel time was 49.07 min, and only 1.83% used public transport. For public transport users, individuals with a high percentage of public transport use and ride-sourcing used it only for travel and working/studying. Hence, although the number of passengers using public transport was minimal, the passenger trip purpose was working, a mandatory activity that strongly influences household income. Urban planners should prioritize public transport operations despite potential losses. However, public transportation can play a significant role in transmitting infectious diseases and can also hinder the ability of a city to respond to new outbreaks (Connolly et al., 2020). To ensure that public transportation can continue to operate, it is essential to explore effective measures for reducing the transmission of viruses during travel.

6. Conclusions

Based on the study we conducted to determine activity-need fulfillment during the COVID-19 pandemic, the following conclusions can be drawn.

• Among working/studying, sports, leisure, and shopping, the activity needs were unfulfilled for working/studying, sports, and leisure.

• There is a significant variation of activity-need value among groups based on sociodemographic indicators. Individuals with low income and older than 55 years have a greater risk of having unmet activity needs than those with the other indicators.

• Fulfillment of activity needs grouped based on the built environment varies significantly, primarily depending on the distance to public facilities and the residential environment characteristics/quality. Those living far from essential public facilities, such as the city center, administration offices, schools, hospitals, and modern markets, tend to experience high unfulfilled activity needs. Additionally, individuals residing in residential areas with good public facilities, including supermarkets, traditional markets, and schools, have higher unfulfilled activity needs than those living in other residential areas.

• Access to public transportation is essential during a pandemic as people rely on it for work.

Shifting from working/studying outside the home to doing so within the home may help alleviate traffic congestion because these activities tend to contribute more to traffic than others. However, it is important to carefully consider this option, as experts believe that an individual’s resources and abilities are critical factors in determining whether they need to work or study outside the home. Therefore, prioritizing the development of these resources and abilities may be a helpful first step toward decreasing the need for out-of-home working/studying.

The limitation of the study was the use of nonprobability sampling, which may lead to assumptions that the findings cannot be applied to the whole population. While it is very common to have a convenience sampling with specific target in this kind of study, it is needed as a continuation study to have a more varied group of community as an object of study.

Analyzing the activity need is vital in understanding a component of an individual’s well-being. This study focuses on exploring the activity needs by using descriptive and bivariate analyses with data from the Surakarta agglomeration area during the COVID-19 pandemic, specifically on the fulfillment of activity needs because of the transition from out-of-home to in-home activities. Further research is necessary to develop the theory, including conducting multivariate analysis to define the significant variables related to activity needs.

Acknowledgments

We want to thank to the Chancellor of Sebelas Maret University, who has given the decision that this research is funded by the RKAT PTNBH Universitas Sebelas Maret Fiscal Year 2023 through a Research scheme PENELITIAN DISERTASI DOKTOR (PDD-UNS) under Grant Number: 228/UN27.22/PT.01.03/2023.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

Additional information

Notes on contributors

Amirotul Musthofiah Hidayah Mahmudah

Amirotul Musthofiah Hidayah Mahmudah is a lecturer in the civil engineering department of Sebelas Maret University. She received a B.Sc. degree in civil engineering from Sebelas Maret University (1994) and an M.Sc. in transportation engineering from the IHE Delft Netherland (2000). Currently, a Doctoral student working on transport behavior at Sebelas Maret University. Research interests include traffic engineering, public transport, and traffic safety.

Arif Budiarto

Arif Budiarto is an associate professor in the civil engineering department at Sebelas Maret University. He received a bachelor’s degree in civil engineering in 1989. He had a master’s degree in 1998 and a Doctorate in 2011 from Bandung Institute of Technology. His research interests are in road pavement and material, and travel behavior.

Tri Basuki Joewono

Tri Basuki Joewono is a Professor of Civil Engineering at the Department of Civil Engineering, Parahyangan Catholic University, Bandung, Indonesia, and served as Vice Rector for Academic Affairs from 2019 to 2023. He received a bachelor’s degree in 1997 and a master’s degree 1999 in civil engineering from the Parahyangan Catholic University, Indonesia. He had a master’s degree in transportation, specializing in transportation systems, in 2002 from Bandung Institute of Technology, Indonesia. He had a Ph.D. in Civil & Environmental Engineering, specializing in Transportation Engineering and Planning, from Saitama University, Japan 2007. The research interests are Public Transport, Transport Behaviour, Transport Modelling, Road Safety, and Transport in Developing Countries.

Tri Hardiyanti Asmaningrum

Tri Hardiyanti Asmaningrum graduated with a bachelor of civil engineering and planning education from Yogyakarta state university (2020) and a Master’s degree in specialized of transportation from Sebelas Maret University (2023). Her research interest focuses on investigated behavior of activity satisfaction, time fixity in different context and public transport planning.

Dimas Bayu Endrayana Dharmowijoyo

Dimas Bayu Endrayana Dharmowijoyo is an engineer at National Distribution Services, Australia, and a researcher at Universitas Janabadra, Indonesia. He received a Ph.D. in 2016 from the KTH Royal Institute of Technology, Sweden. His research interest is investigating the complexity and variability of individuals’ activity-travel patterns and their effects on well-being and health.