  Accurate modeling of human time-activity trajectory is essential to support
community resilience and emergency response strategies such as daily energy
planning and urban seismic vulnerability assessment. However, existing modeling
of time-activity trajectory is only driven by socio-demographic information
with identical activity trajectories shared among the same group of people and
neglects the influence of the environment. To further improve human
time-activity trajectory modeling, this paper constructs community
time-activity trajectory and analyzes how social-demographic and built
environment influence people s activity trajectory based on Markov Chains and
Dirichlet Regression. We use the New York area as a case study and gather data
from American Time Use Survey, Policy Map, and the New York City Energy & Water
Performance Map to evaluate the proposed method. To validate the regression
model, Box s M Test and T-test are performed with 80% data training the model
and the left 20% as the test sample. The modeling results align well with the
actual human behavior trajectories, demonstrating the effectiveness of the
proposed method. It also shows that both social-demographic and built
environment factors will significantly impact a community's time-activity
trajectory. Specifically, 1) Diversity and median age both have a significant
influence on the proportion of time people assign to education activity. 2)
Transportation condition affects people s activity trajectory in the way that
longer commute time decreases the proportion of biological activity (eg.
sleeping and eating) and increases people s working time. 3) Residential
density affects almost all activities with a significant p-value for all
biological needs, household management, working, education, and personal
preference.
