ABSTRACT:
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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Lu Lin, Jianxin Li , Feng
Chen, Jieping Ye, Senior Member, IEEE, and Jinpeng Huai, “Road Traffic Speed
Prediction: A Probabilistic Model Fusing Multi-Source Data”, IEEE TRANSACTIONS
ON KNOWLEDGE AND DATA ENGINEERING, VOL. 30, NO. 7, JULY 2018.