.png){kind=icon}
返回
Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2017.2718525.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Road traffic speed prediction is a challenging problem in intelligent transportation system (ITS) and has gained increasing attentions. Existing works are mainly based on raw speed sensing data obtained from infrastructure sensors or probe vehicles, which, however, are limited by expensive cost of sensor deployment and maintenance. With sparse speed observations, traditional methods based only on speed sensing data are insufficient, especially when emergencies like traffic accidents occur. To address the issue, this paper aims to improve the road traffic speed prediction by fusing traditional speed sensing data with new-type sensing data from cross domain sources, such as tweet sensors from social media and trajectory sensors from map and traffic service platforms. Jointly modeling information from different datasets brings many challenges, including location uncertainty of low-resolution data, language ambiguity of traffic description in texts, and heterogeneity of cross-domain data. In response to these challenges, we present a unified probabilistic framework, called Topic-Enhanced Gaussian Process Aggregation Model (TEGPAM), consisting of three components, i.e., location disaggregation model, traffic topic model, and traffic speed Gaussian Process model, which integrate new-type data with traditional data. Experiments on real world data from two large cities validate the effectiveness and efficiency of our model.
Keyword:
Traffic prediction
gaussian process
topic modeling
multi-source data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
分享收藏
IoT Based Solar Panel Analysis using Thermal Imaging.png)
2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)
2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)IF0
PREAI
分享收藏Anisotropic Etching of Crystalline Silicon in Alkaline Solutions: I . Orientation Dependence and Behavior of Passivation Layers晶体硅在碱性溶液中的各向异性蚀刻: I.钝化层的取向依赖性和行为
PREAI
分享收藏
查看更多内容