Efficient Traffic Estimation With Multi-Sourced Data by Parallel Coupled Hidden Markov Model
Efficient Traffic Estimation With Multi-Sourced Data by Parallel Coupled Hidden Markov Model
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基于并行耦合隐马尔可夫模型的多源数据高效流量估计
DOI：
10.1109/tits.2018.2870948
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发表时间：
2019-08
影响因子：
8.5
通讯作者：
Zhiqiu Huang
中科院分区：
文献类型：
--
作者：
Senzhang Wang;Xiaoming Zhang;Fenxiang Li;Philip S. Yu;Zhiqiu Huang
具有稀疏GPS探针数据的动脉网络中的交通拥堵估计实际上是一个重要的，同时具有挑战性的研究问题。现有GPS探针基于数据的流量估计模型的有效性和效率是LA
Traffic congestion estimation in arterial networks with sparse GPS probe data is a practically important while substantially challenging research issue. The effectiveness and efficiency of the existing GPS probe data-based traffic estimation models are largely limited due to the following two challenges. First, due to the low sampling frequency of GPS probes, probe data are usually sparse, especially for some road links not located in the central urban areas. Second, due to the very complex temporal and spatial dependencies among the road links, the variable space of the existing traffic estimation models is huge. It is time consuming to get an accurate estimation of a large arterial road network with thousands of road links. To address the abovementioned issues, this paper proposes to extract traffic event signals from social media and incorporate them with GPS probe data to alleviate the data sparse issue. We first collect traffic-related posts that report various traffic events, including traffic jam, accident, and road construction from Twitter. By considering the GPS probe readings and the traffic event tweets as two types of observations, we next extend the conventional coupled hidden Markov model for integrating the two types of data to obtain a more accurate estimation of traffic conditions. To address the computational challenge, a parallel importance sampling-based electromagnetic algorithm is further introduced. We evaluate our model on the arterial network of downtown Chicago. The experimental results demonstrate the superior performance of the model in both effectiveness and efficiency.
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DOI：
10.1109/itst.2006.288722
发表时间：
2006-06
期刊：
2006 6th International Conference on ITS Telecommunications
影响因子：
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作者：
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通讯作者：
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发表时间：
2014-11
期刊：
Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
影响因子：
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作者：
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通讯作者：
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发表时间：
2012-05
期刊：
TCRP Synthesis of Transit Practice
影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
2012-11
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/tits.2017.2650947
发表时间：
2014-11
影响因子：
8.5
作者：
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通讯作者：
Nicholas G. Polson;Vadim O. Sokolov