基于N阶近邻路网的车辆行程时间估计模型

N-order Neighbor Road Network Based Vehicle Travel Time Estimation Model

车联网的提出为智能交通的研究提供了新的交通信息收集技术.针对短时交通中车辆的路网行程时间估计问题,提出了基于N阶近邻的隐马尔科夫模型,利用马尔科夫性质来解决道路行程时间的前后关联性问题,同时考虑不同道路的异构性构建了N阶近邻路网模型来模拟路网间的交互影响.针对短时交通中实时数据更新的问题,提出基于道路关联性算法,并结合车联网的采集技术给出了迭代更新模型的方法.实验表明,本文提出的方法在短时交通车辆行程时间预测中精度较高,能够在车辆行进中做出实时预测.

The development of Internet of vehicles provides a new traffic information collection technique for the study of intelligent transportation.In this article,we propose an A＇-order hidden Markov model to approach the vehicle travel time prediction problem,utilizing the Markov nature to model the internship of road network.We also promote an N-order neighbor road network to address the heterogeneity of road.A non-trivia update algorithm is applied to handle the real time data approaching issue.We also prove the temporality of the N-order hidden Markov model in travel time prediction.Experimental results on authentic data indicate the effectiveness and accuracy of this approach.