Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic
  Signal Control   Traffic congestion at a signalized intersection greatly reduces the travel
time reliability in urban areas. Adaptive signal control system (ASCS) is the
most advanced traffic signal technology that regulates the signal phasing and
timings considering the patterns in real-time in order to reduce congestion.
Real-time prediction of queue lengths can be used to adjust the phasing and
timings for different movements at an intersection with ASCS. The accuracy of
the prediction varies based on the factors, such as the stochastic nature of
the vehicle arrival rates, time of the day, weather and driver characteristics.
In addition, accurate prediction for multilane, undersaturated and saturated
traffic scenarios is challenging. Thus, the objective of this study is to
develop queue length prediction models for signalized intersections that can be
leveraged by ASCS using four variations of Grey systems: (i) the first order
single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error
corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier
error corrections. The efficacy of the GM is that they facilitate fast
processing; as these models do not require a large amount of data; as would be
needed in artificial intelligence models; and they are able to adapt to
stochastic changes, unlike statistical models. We have conducted a case study
using queue length data from five intersections with ASCS on a calibrated
roadway network in Lexington, South Carolina. GM were compared with linear,
nonlinear time series models, and long short-term memory (LSTM) neural network.
Based on our analyses, we found that EGVM reduces the prediction error over
closest competing models (i.e., LSTM and time series models) in predicting
average and maximum queue lengths by 40% and 42%, respectively, in terms of
Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean
Absolute Error.

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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: econ