Dynamic prediction of drivers' personal routes through machine learning

Yue Dai, Yuan Ma, Qianyi Wang, Y. Murphey, Shiqi Qiu, Johannes Kristinsson, Jason Meyer, F. Tseng, T. Feldkamp
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引用次数: 3

Abstract

Personal route prediction (PRP) has attracted much research interest recently because of its technical challenges and broad applications in intelligent vehicle and transportation systems. Traditional navigation systems generate a route for a given origin and destination based on either shortest or fastest route schemes. In practice, different people may very likely take different routes from the same origin to the same destination. Personal route prediction attempts to predict a driver's route based on the knowledge of driver's preferences. In this paper we present an intelligent personal route prediction system, I_PRP, which is built based upon a knowledge base of personal route preference learned from driver's historical trips. The I_PRP contains an intelligent route prediction algorithm based on the first order Markov chain model to predict a driver's intended route for a given pair of origin and destination, and a dynamic route prediction algorithm that has the capability of predicting driver's new route after the driver departs from the predicted route.
通过机器学习动态预测驾驶员个人路线
个人路线预测(Personal route prediction, PRP)由于其技术挑战和在智能车辆和交通系统中的广泛应用,近年来引起了广泛的研究兴趣。传统的导航系统根据最短或最快的路线方案为给定的起点和目的地生成路线。在实践中,不同的人很可能从同一个起点到同一个目的地走不同的路线。个人路线预测试图根据驾驶员的偏好来预测驾驶员的路线。本文提出了一种基于驾驶员历史行程中个人路线偏好知识库的智能个人路线预测系统I_PRP。I_PRP包含一种基于一阶马尔可夫链模型的智能路线预测算法,用于预测驾驶员在给定起点和目的地的预期路线,以及一种动态路线预测算法,具有预测驾驶员离开预测路线后的新路线的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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