过度通勤的分解：一种蒙特卡罗模拟方法
Decomposing Excess Commuting: A Monte Carlo Simulation Approach
摘要：
过度或浪费的通勤是以实际通勤超过最小（最佳）通勤的比例来衡量的，假设人们可以自由地在一个城市交换他们的家园和工作。研究通常依赖于调查数据来定义实际通勤，并通过线性规划（LP）来衡量总分区水平上的最佳通勤。旅行时间从一个调查可能包括报告错误和受访者可能不代表他们居住的地区，并得出最佳通勤在总面积水平也受到地带效应。两者都可能使过度通勤的估计产生偏差。基于2006-2010年路易斯安那州巴吞鲁日的交通规划包（CTPP）数据，本研究使用蒙特卡罗方法模拟人口普查区内的个人居民工人和个人工作，估计通勤距离和时间从旅行到工作行程，并根据模拟的个人位置定义最佳通勤。研究结果表明，报告错误和使用汇总分区数据有助于误算过度通勤。
英文摘要：
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.
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