论文标题
分解多余的通勤：蒙特卡洛模拟方法
Decomposing Excess Commuting: A Monte Carlo Simulation Approach
论文作者
论文摘要
在假设人们可以在城市中自由交换房屋和工作时，过多或浪费的通勤是实际通勤的比例。研究通常依靠调查数据来定义实际通勤，并通过线性编程（LP）在综合层面上测量最佳通勤。调查的旅行时间可能包括报告错误，并且受访者可能无法代表他们所居住的领域；并且在汇总水平上衍生的最佳通勤也符合区域效应。两者都可能偏向过多通勤的估计。基于路易斯安那州巴吞鲁日（Baton Rouge）的2006年至2010年人口普查（CTPP）数据，该研究采用蒙特卡洛（Monte Carlo）方法来模拟人口普查区域内的个人常驻工人和个人工作，估计通勤距离和与工作之旅旅行的时间，并根据模拟各个位置定义最佳通勤。调查结果表明，报告误差和使用汇总数据数据的使用都导致过度通勤的错误计算。
Excess or wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. Studies usually rely on survey data to define actual commute, and measure the optimal commute at an aggregate zonal level by linear programming (LP). Travel time from a survey could include reporting errors and respondents might not be representative of the areas they reside; and the derived optimal commute at an aggregate areal level is also subject to the zonal effect. Both may bias the estimate of excess commuting. Based on the 2006-2010 Census for Transportation Planning Package (CTPP) data in Baton Rouge, Louisiana, this research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts, estimate commute distance and time from journey-to-work trips, and define the optimal commute based on simulated individual locations. Findings indicate that both reporting errors and the use of aggregate zonal data contribute to miscalculation of excess commuting.
