基于Markov链模拟和Dirichlet回归的社区时间-活动轨迹建模
Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression
摘要：
人类时间-活动轨迹的精确建模对于支持社区复原力和应急响应策略（如日常能源规划和城市地震脆弱性评估）至关重要。然而，现有的时间-活动轨迹建模仅由社会人口统计信息驱动，同一组人共享相同的活动轨迹，忽略了环境的影响。为进一步完善人类时间-活动轨迹模型，构建了社区时间-活动轨迹，并基于马尔可夫链和狄利克雷回归分析了社会人口统计和建成环境对人类活动轨迹的影响。我们使用纽约地区作为案例研究，并收集数据，从美国时间使用调查，政策地图，纽约市能源和水性能地图，以评估所提出的方法。为了验证回归模型，用80%的数据训练模型，剩下的20%作为测试样本，进行Box s M检验和T检验。建模结果与实际人体行为轨迹吻合较好，验证了该方法的有效性。它还表明，社会人口和建筑环境因素将显着影响社区的时间活动轨迹。具体而言，1）多样性和中位数年龄都对人们分配给教育活动的时间比例有显着影响。2)交通条件影响人们的活动轨迹，因为较长的通勤时间降低了生物活动的比例（例如，吃，吃。3)居住密度影响几乎所有活动，对所有生物需求，家庭管理，工作，教育和个人偏好都有显着的p值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
