Spatial Transferability of Person-Level Daily Activity Generation and Time Use Models

2013 ◽  
Vol 2343 (1) ◽  
pp. 95-104 ◽  
Author(s):  
Sujan Sikder ◽  
Abdul Rawoof Pinjari
Keyword(s):  
Time Use ◽  
Daily Activity ◽  
Activity Generation ◽  
Spatial Transferability

scholarly journals Q-Learning for Flexible Learning of Daily Activity Plans

2005 ◽  
Vol 1935 (1) ◽  
pp. 163-169 ◽  
Author(s):  
David Charypar ◽  
Kai Nagel
Keyword(s):  
Daily Activity ◽  
Marginal Utility ◽  
Activity Time ◽  
Learning Problem ◽  
Q Learning ◽  
Work Shop ◽  
Home Work ◽  
Activity Generation ◽  
Transportation Simulation ◽  
Transportation Activity

Q-learning is a method from artificial intelligence to solve the reinforcement learning problem (RLP), defined as follows. An agent is faced with a set of states, S. For each state s there is a set of actions, A( s), that the agent can take and that takes the agent (deterministically or stochastically) to another state. For each state the agent receives a (possibly stochastic) reward. The task is to select actions such that the reward is maximized. Activity generation is for demand generation in the context of transportation simulation. For each member of a synthetic population, a daily activity plan stating a sequence of activities (e.g., home-work-shop-home), including locations and times, needs to be found. Activities at different locations generate demand for transportation. Activity generation can be modeled as an RLP with the states given by the triple (type of activity, starting time of activity, time already spent at activity). The possible actions are either to stay at a given activity or to move to another activity. Rewards are given as “utility per time slice,” which corresponds to a coarse version of marginal utility. Q-learning has the property that, by repeating similar experiences over and over again, the agent looks forward in time; that is, the agent can also go on paths through state space in which high rewards are given only at the end. This paper presents computational results with such an algorithm for daily activity planning.

scholarly journals Activity-Travel Behaviour, and Daily and Global Well-Being

2018 ◽  
Vol 203 ◽  
pp. 05004 ◽  
Author(s):  
Muhammad Isran Ramli ◽  
Dimas Endrayana Dharmowijoyo
Keyword(s):  
Time Use ◽  
Daily Activity ◽  
Travel Behaviour ◽  
Well Being ◽  
Activity Participation ◽  
Grocery Shopping ◽  
Subjective Well Being ◽  
People’S Daily ◽  
Life Circumstances ◽  
People's Daily

Using a hierarchical SEM and multidimensional 3-week household time-use and activity diary, this study investigated how interaction of individuals’ daily travel parameters, time-use and activity participation and percentage of undertaking passive leisure within various activity participation, life circumstances, and geographical conditions shape individuals’ daily and global subjective well-being. This study confirms that life circumstances insignificantly shape people’s well-being as argued as well in previous studies. Moreover, daily subjective well-being or people daily context in which contains how people organizes their daily activity-travel behaviour positively shape people life satisfaction as hypothesised. This study also confirms that different daily activity participation tends to shape different level of people’s daily subjective well-being. Spending more time-use for leisure, sport and grocery shopping tends to positively correlate with having better daily subjective well-being. Having better mental and social health are found to positively shape people’s daily and global well-being, respectively. For policy implementations, this study can say that providing more opportunities for undertaking out-of-home activities such as out-of-home leisure, sport and grocery shopping with time-use policy and denser land use planning.

Exploration of Time Use Utility Derived by Older Individuals from Daily Activity–Travel Patterns

2010 ◽  
Vol 2156 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Sarah Elia Ziems ◽  
Karthik C. Konduri ◽  
Bhargava Sana ◽  
Ram M. Pendyala
Keyword(s):  
Time Use ◽  
Daily Activity ◽  
Older Individuals ◽  
Travel Patterns

scholarly journals Balancing time use for children’s fitness and adiposity: Evidence to inform 24-hour guidelines for sleep, sedentary time and physical activity

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245501
Author(s):  
Dorothea Dumuid ◽  
Melissa Wake ◽  
David Burgner ◽  
Mark S. Tremblay ◽  
Anthony D. Okely ◽  
...  
Keyword(s):  
Physical Activity ◽  
Time Use ◽  
Daily Activity ◽  
Sedentary Time ◽  
Vigorous Physical Activity ◽  
Population Based ◽  
Optimal Time ◽  
Cross Sectional ◽  
Trade Offs ◽  
Log Ratio

Purpose Daily time spent on one activity cannot change without compensatory changes in others, which themselves may impact on health outcomes. Optimal daily activity combinations may differ across outcomes. We estimated optimal daily activity durations for the highest fitness and lowest adiposity. Methods Cross-sectional Child Health CheckPoint data (1182 11-12-year-olds; 51% boys) from the population-based Longitudinal Study of Australian Children were used. Daily activity composition (sleep, sedentary time, light physical activity [LPA], moderate-to-vigorous physical activity [MVPA]) was from 8-day, 24-hour accelerometry. We created composite outcomes for fitness (VO2max; standing long jump) and adiposity (waist-to-height ratio; body mass index; fat-to-fat-free log-ratio). Adjusted compositional models regressed activity log-ratios against each outcome. Best activity compositions (optimal time-use zones) were plotted in quaternary tetrahedrons; the overall optimal time-use composition was the center of the overlapping area. Results Time-use composition was associated with fitness and adiposity (all measures p<0.001). Optimal time use differed for fitness and adiposity. While both maximized MVPA and minimized sedentary time, optimal fitness days had higher LPA (3.4 h) and shorter sleep (8.25 h), but optimal adiposity days had lower LPA (1.0 h) and longer sleep (10.9 h). Balancing both outcomes, the overall optimal time-use composition was (mean [range]): 10.2 [9.5; 10.5] h sleep, 9.9 [8.8; 11.2] h sedentary time, 2.4 [1.8; 3.2] h LPA and 1.5 [1.5; 1.5] h MVPA. Conclusion Optimal time use for children’s fitness and adiposity involves trade-offs. To best balance both outcomes, estimated activity durations for sleep and LPA align with, but for MVPA exceed, 24-h guidelines.

Exploratory Analysis of the Activity Time-Use Frontier and Its Effect on Episodic Well-Being: Data from the Disability and Use of Time Survey

2017 ◽  
Vol 2669 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Srinath K. Ravulaparthy ◽  
Karthik C. Konduri ◽  
Konstadinos G. Goulias
Keyword(s):  
Time Use ◽  
Daily Activity ◽  
Negative Emotions ◽  
Well Being ◽  
Time Budgets ◽  
Activity Time ◽  
Technical Inefficiency ◽  
Activity Engagement ◽  
Use Patterns ◽  
Use Of Time

The role of time (as a constrained resource) in terms of budgets and expenditures is of great importance in travel behavior analysis within the context of daily activity engagement choices, emotional well-being, and quality of life. This research investigated the behavioral links between activity time budgets and episodic well-being measures in a two-stage process, using data from the 2009 Disability and Use of Time Survey. First, with the use of the episodic-level data, time budgets were formulated with the use of a stochastic frontier modeling approach. The technical inefficiency measure that represented the degree to which an individual expended his or her time (or an upper bound of the time budget) in activity engagement was also derived. Second, with the use of this measure of technical inefficiency, the effects on reported individuals’ episodic well-being measures were further investigated. The indicators of well-being—happiness, calmness, frustration, sadness, worry, tiredness, and pain—were analyzed with a multivariate ordered probit modeling framework. The models were estimated by controlling for a broad array of covariates related to sociodemographics, activity, and travel characteristics, along with the social contexts of companionship and altruism and global well-being indicators. Empirical results suggested that individuals experienced varying levels of positive and negative emotions from their daily activity time-use patterns, in both efficient and inefficient episodes. Productive episodes (e.g., working and volunteering) with higher time budgets (or inefficiencies) increased the likelihood of individuals experiencing higher levels of negative emotions. The model findings also revealed that high-income households and individuals younger than 65 years old with inefficient time-use patterns exhibited lower levels of happiness and calmness.

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