Origin-Destination matrices obtained from smartcard data are very valuable because they contain vast amounts of information and can be obtained at a very low cost. However, they can only account for trips paid by smartcard. Trips paid by other means, as well as non-paid trips, must be incorporated using additional information. This paper discusses the biases that are introduced due to fare evasion and presents a sequential method to estimate correction factors due to partial evasion in some trip stages, as well as total fare evasion in all trip stages, using external information regarding trips not registered in the smartcard database. We apply this method to the case of Santiago, Chile, where partial evasion (during a bus trip stage prior to a Metro trip stage) and total evasion (during all bus-only trip stages) are relevant fare evasion situations. Information from the Santiago Metro Origin-Destination survey and from external fare evasion measurements is used. The results indicate a 5% partial evasion rate for bus trip stages prior to Metro trip stages, and a 37% total fare evasion rate for bus-only trips. This paper is a contribution towards establishing new methods to feasibly obtain OD matrices through the adequate merging of automatically-collected data with complementary traditional measurements and survey instruments.

从智能卡数据获得的起点-目的地矩阵非常有价值，因为它们包含大量信息并且可以以非常低的成本获得。但是，他们只能考虑由智能卡支付的旅程。必须使用其他信息将通过其他方式支付的旅行以及非付费旅行纳入其中。本文讨论了由于逃票而引起的偏差，并提出了一种顺序方法来估计由于某些行程阶段的部分逃逸以及所有行程阶段的总逃票而产生的校正系数，并使用有关未在旅程中注册的行程的外部信息。智能卡数据库。我们将此方法应用于智利圣地亚哥的情况，其中部分逃逸（在地铁出行之前的公交出行阶段）和完全逃逸（在所有仅公交出行的阶段）是相关的逃票情况。使用来自圣地亚哥地铁始发地-目的地调查和外部逃票测量的信息。结果表明，在地铁出行阶段之前，公交出行阶段的部分规避率为5％，仅公交出行的总票价规避率为37％。本文是对建立新方法的一种贡献，该方法是通过将自动收集的数据与互补的传统测量和调查工具充分合并，切实可行地获得OD矩阵。结果表明，在地铁出行阶段之前，公交出行阶段的部分规避率为5％，仅公交出行的总票价规避率为37％。本文是对建立新方法的一种贡献，该方法是通过将自动收集的数据与互补的传统测量和调查工具充分合并，切实可行地获得OD矩阵。结果表明，在地铁出行阶段之前，公交出行阶段的部分规避率为5％，仅公交出行的总票价规避率为37％。本文是对建立新方法的一种贡献，该方法是通过将自动收集的数据与互补的传统测量和调查工具充分合并，切实可行地获得OD矩阵。