论文标题
基于马尔可夫链模拟和迪里奇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
论文作者
论文摘要
人类时间活性轨迹的准确建模对于支持社区弹性和应急响应策略，例如日常能源计划和城市地震脆弱性评估至关重要。但是，现有的时间活动轨迹建模仅受社会人口统计信息的驱动，而同一人群之间共享的活动轨迹相同，并忽略了环境的影响。为了进一步改善人类的时间活性轨迹建模，本文构建了社区的时间活动轨迹，并分析了社会人口统计学和建筑环境如何影响基于马尔可夫链和迪里奇回归的人们的活动轨迹。我们将纽约地区作为案例研究，并从美国时间使用调查，政策图和纽约市能源与水性能图中收集数据来评估所提出的方法。为了验证回归模型，使用80％的数据训练模型进行框S M检验和t检验，将左20％作为测试样本。建模结果与实际的人类行为轨迹很好地吻合，证明了所提出的方法的有效性。它还表明，社会人口统计学和建筑环境因素都会显着影响社区的时间活动轨迹。具体而言，1）多样性和中位年龄都对人们分配给教育活动的时间的比例都有很大的影响。 2）运输条件以更长的通勤时间降低生物活动的比例（例如睡眠和饮食）的方式影响人们的活动轨迹，并增加了人们的工作时间。 3）住宅密度几乎影响了所有活动，以满足所有生物学需求，家庭管理，工作，教育和个人喜好的重要p值。
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.
