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Building efficient probability transition matrix using machine learning from big data for personalized route prediction

机译：基于个性化路径预测的大数据的机器学习建立高效概率转换矩阵

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摘要

Personalized route prediction is an important technology in many applications related to intelligent vehicles and transportation systems. Current route prediction technologies used in many general navigation systems are, by and large, based on either the shortest or the fastest route selection. Personal traveling route prediction is a very challenging big data problem, as trips getting longer and variations in routes growing. It is particularly challenging for real-time in-vehicle applications, since many embedded processors have limited memory and computational power. In this paper we present a machine learning algorithm for modeling route prediction based on a Markov chain model, and a route prediction algorithm based on a probability transition matrix. We also present two data reduction algorithms, one is developed to map large GPS based trips to a compact link-based standard route representation, and another a machine learning algorithm to significantly reduce the size of a probability transition matrix. The proposed algorithms are evaluated on real-world driving trip data collected in four months, where the data collected in the first three months are used as training and the data in the fourth month are used as testing. Our experiment results show that the proposed personal route prediction system genèrated more than 91% prediction accuracy in average among the test trips. The data reduction algorithm gave about 8:1 reduction in link-based standard route representation and 23:1 in reducing the size of probability transition matrix.
机译：个性化的路线预测是与智能车辆和运输系统有关的许多应用中的重要技术。基于最短或最快的路由选择，许多通用导航系统中使用的当前路线预测技术是且大的。个人旅行路线预测是一个非常具有挑战性的大数据问题，因为旅行越来越长，途径变化而生长。它对实时车载应用尤其具有挑战性，因为许多嵌入式处理器具有有限的存储器和计算能力。本文介绍了一种基于Markov链模型的途径预测的机器学习算法，以及基于概率转换矩阵的路径预测算法。我们还呈现了两个数据减少算法，开发了一个基于GPS的大型GPS映射到基于紧凑的链路的标准路由表示，并且另一个机器学习算法显着降低了概率转换矩阵的大小。该算法在四个月内收集的现实世界驾驶跳闸数据进行评估，其中在前三个月收集的数据用作培训，第四个月中的数据用作测试。我们的实验结果表明，在试验之旅中，建议的个人路线预测系统平均平均预测精度超过91％的预测精度。数据减少算法在降低概率转换矩阵的大小时，在基于链路的标准路由表示和23：1中进行了大约8：1。

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