9,970 research outputs found
Hybrid hidden Markov LSTM for short-term traffic flow prediction
Deep learning (DL) methods have outperformed parametric models such as
historical average, ARIMA and variants in predicting traffic variables into
short and near-short future, that are critical for traffic management.
Specifically, recurrent neural network (RNN) and its variants (e.g. long
short-term memory) are designed to retain long-term temporal correlations and
therefore are suitable for modeling sequences. However, multi-regime models
assume the traffic system to evolve through multiple states (say, free-flow,
congestion in traffic) with distinct characteristics, and hence, separate
models are trained to characterize the traffic dynamics within each regime. For
instance, Markov-switching models with a hidden Markov model (HMM) for regime
identification is capable of capturing complex dynamic patterns and
non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an
observation sequence from a set of latent or, hidden state variables. In LSTM,
the latent variable is computed in a deterministic manner from the current
observation and the previous latent variable, while, in HMM, the set of latent
variables is a Markov chain. Inspired by research in natural language
processing, a hybrid hidden Markov-LSTM model that is capable of learning
complementary features in traffic data is proposed for traffic flow prediction.
Results indicate significant performance gains in using hybrid architecture
compared to conventional methods such as Markov switching ARIMA and LSTM
The Prediction and Mitigation of Road Traffic Congestion Based on Machine Learning
Traffic congestion is a major issue for all developed countries. In most urbanised areas space is a scarce commodity. Therefore, better management of the existing roads to increase or maintain their capacity level is the only viable solution. Research in the last two decades has focused on Intelligent Transport Systems (ITS) development. Predicting traffic flow in real time can be used to prevent or alleviate future congestion. The key to an effective proactive method is a model that produces timely and accurate predictions. However, despite extensive research in this area, a reliable method is still not available. Therefore, in this thesis, we developed an accurate online road traffic flow prediction model, with a particular focus on heterogeneous traffic flow, for urbanised road networks. The contributions of this work include:
Firstly, we conducted a comprehensive literature review and benchmark evaluation of existing machine learning models using a real dataset obtained from Transport for Greater Manchester. We investigated their prediction accuracy, time horizon sensitivity, and input feature settings (different classes of vehicles), to understand how they can affect their prediction accuracy. The experimental results show that the artificial neural network was the most successful at predicting short-term road traffic flow. Additionally, it was found that different classes of vehicles can improve prediction accuracy.
Secondly, we examined three recurrent neural networks (a standard recurrent, a long short-term memory, and a gated recurrent unit). We compared their accuracy, training time, and sensitivity to architectural change using a new performance metric we developed to standardise the accuracy and training time into a comparable score (STATS). The experimental results show that the gated recurrent unit performed the best and was most stable against architectural changes. Conversely, the long short-term memory was the least stable model.
Thirdly, we investigated different magnitudes of temporal patterns in the dataset, both short and long-term, to understand how contextual temporal data can improve prediction accuracy. We also developed a novel online dynamic temporal context neural network framework. The framework dynamically determines how useful a temporal data segment is for prediction, and weights it accordingly for use in the regression model. The experimental results show that short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved prediction results by 10.8% when compared with a deep gated recurrent model.
Finally, we investigated the dynamic nature of road traffic flowâs input features by examining their spatial and temporal relationships. We also developed a novel dynamic exogenous feature filter mechanism. The feature filter mechanism uses âlocal windowsâ to filter input features in real-time to improve prediction accuracy. The results show that a global correlation was insufficient to describe the complex and dynamic relationships between the input features. The local correlations (local windows) were able to identify additional geospatial and temporal relationships. Furthermore, the proposed feature filter mechanism was compared to a state-of-the-art method, a dynamic rolling window feature filter model. The experimental results showed that the proposed model was the most accurate, with an RMSE of 10.06%, closely followed by the dynamic rolling window feature filter model, with an RMSE of 10.98%. However, the proposed model was computationally much lighter than the rolling windows model
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Network Traffic Matrix (TM) prediction is defined as the problem of
estimating future network traffic from the previous and achieved network
traffic data. It is widely used in network planning, resource management and
network security. Long Short-Term Memory (LSTM) is a specific recurrent neural
network (RNN) architecture that is well-suited to learn from experience to
classify, process and predict time series with time lags of unknown size. LSTMs
have been shown to model temporal sequences and their long-range dependencies
more accurately than conventional RNNs. In this paper, we propose a LSTM RNN
framework for predicting short and long term Traffic Matrix (TM) in large
networks. By validating our framework on real-world data from GEANT network, we
show that our LSTM models converge quickly and give state of the art TM
prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with
arXiv:1402.1128 by other author
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN
This paper presents NeuTM, a framework for network Traffic Matrix (TM)
prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM
RNNs). TM prediction is defined as the problem of estimating future network
traffic matrix from the previous and achieved network traffic data. It is
widely used in network planning, resource management and network security. Long
Short-Term Memory (LSTM) is a specific recurrent neural network (RNN)
architecture that is well-suited to learn from data and classify or predict
time series with time lags of unknown size. LSTMs have been shown to model
long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM
RNN-based framework for predicting TM in large networks. By validating our
framework on real-world data from GEEANT network, we show that our model
converges quickly and gives state of the art TM prediction performance.Comment: Submitted to NOMS18. arXiv admin note: substantial text overlap with
arXiv:1705.0569
Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction
Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A
significant number of algorithms and models have been put forward to analyse
traffic data and make prediction. In the recent big data era, deep learning has
been exploited to mine the profound information hidden in the data. In
particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network
(RNN) schemes, has attracted a lot of attentions due to its capability of
processing the long-range dependency embedded in the sequential traffic data.
However, LSTM has considerable computational cost, which can not be tolerated
in tasks with stringent latency requirement. In this paper, we propose a deep
learning model based on LSTM, called Random Connectivity LSTM (RCLSTM).
Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the
formation of neural network, which is that the neurons are connected in a
stochastic manner rather than full connected. So, the RCLSTM, with certain
intrinsic sparsity, have many neural connections absent (distinguished from the
full connectivity) and which leads to the reduction of the parameters to be
trained and the computational cost. We apply the RCLSTM to predict traffic and
validate that the RCLSTM with even 35% neural connectivity still shows a
satisfactory performance. When we gradually add training samples, the
performance of RCLSTM becomes increasingly closer to the baseline LSTM.
Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits
even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure
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