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Mining Travel Time from Smart Card Fare Data

机译：从智能卡票价数据采矿旅行时间

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摘要

The wide applications of smart card payment techniques in public transit systems provide a new way of collecting travel time information. In this paper, one method is proposed to estimate travel times with smart card fare payment data and bus schedule data. The proposed method first classifies two sequential card swipes to infer if they occur at the same stop with Naive Bayesian Classifier (NBC). Travel time is estimated from the NBC results using Maximum Likelihood Estimation (MLE), Dynamic Programming (DP) and Quadratic Programming (QP) methods. In order to solve the problem with imprecise initial parameters, coordinate descent method is applied, which updates parameters and estimate values alternatively until it converges. An experiment with real-world data is designed to quantify the reliability of this algorithm and the outcomes is contrast with GPS data. It shows that the error of this method is small and the convergence is fast.
机译：公共交通系统中智能卡支付技术的广泛应用提供了收集旅行时间信息的新方法。在本文中，提出了一种方法来估计智能卡票价支付数据和总线计划数据的旅行时间。该方法首先将两个顺序卡滑动分类为推断出与NAIVE Bayesian分类器（NBC）的同一站点发生。使用最大似然估计（MLE），动态编程（DP）和二次编程（QP）方法，从NBC结果估计旅行时间。为了解决不精确的初始参数的问题，应用坐标血统方法，该方法可选地更新参数并估计值直到它收敛。具有真实数据的实验旨在量化该算法的可靠性，结果与GPS数据形成对比。它表明这种方法的错误很小，收敛速度很快。

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