检索结果-南通市图书馆

bus arrival time prediction Based on Mixed Model

China Communications 2017年第5期14卷 38-47页

作者： Jinglin Li Jie Gao Yu Yang Heran Wei State Key Laboratory of Network and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.

关键词： bus arrival time prediction traffic delay jitter pattern internet of vehicle

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Route Temporal⁃Spatial Information Based Residual Neural Networks for bus arrival time prediction

引用

Journal of Harbin Institute of Technology(New Series) 2020年第4期27卷 31-39页

作者： Chao Yang Xiaolei Ru Bin Hu Key Laboratory of Road and Traffic Engineering of the Ministry of Education School of Transportation EngineeringTongji UniversityShanghai 201804China

bus arrival time prediction contributes to the quality improvement of public transport *** can arrange departure time effectively if they know the accurate bus arrival time in *** proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target *** residual neural network framework was employed to model the bus route temporal⁃spatial *** was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream *** features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions *** on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

关键词： bus arrival time prediction route temporal⁃spatial information residual neural network recurrent neural network bus trajectory data

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

RHMX:bus arrival time prediction via Mixed Model

引用

Journal of Electronic Research and Application 2021年第6期5卷 30-43页

作者： Fei Jia Huibing Zhang Xiaoli Hu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004Guangxi Zhuang Autonomous RegionChina Teaching Practice Department Guilin University of Electronic TechnologyGuilin 541004Guangxi Zhuang Autonomous RegionChina

With the widespread use of information technologies such as IoT and big data in the transportation business,traditional passenger transportation has begun to transition and upgrade into intelligent transportation,providing passengers with a better riding *** precise bus arrival times is a critical link in achieving urban intelligent *** a result,a mixed model-based bus arrival time prediction model(RHMX)was suggested in this work,which could dynamically forecast bus arrival time based on the input ***,two sub-models were created:bus station stopping time prediction and interstation running time *** former predicted the stopping time of a running bus at each downstream station in an iterative manner,while the latter projected its running time on each downstream road segment(stations as the break points).Using the two models,a group of time series data on interstation running time and bus station stopping time may be *** that,the time series data from the two sub-models was fused using long short-term memory(LSTM)to generate an approximate bus arrival ***,using Kalman filtering,the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival *** experimental findings showed that the suggested model's accuracy and stability were both improved by 35%and 17%,respectively,over AutoNavi and Baidu.

关键词： IoT Big data bus arrival time prediction Long short-term memory Kalman filtering

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：