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In this letter, we propose prediction of primary user spectrum activity by constructing a continuous-time Markov chain model for Erlang-2 distributed channel utilization intervals. Moreover, we propose a maximum-likelihood estima...
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In this letter, we propose prediction of primary user spectrum activity by constructing a continuous-time Markov chain model for Erlang-2 distributed channel utilization intervals. Moreover, we propose a maximum-likelihood estimator for traffic parameters utilized by the predictor. Finally, we analyze the prediction confidence as the probability of the prediction performance that is not affected by traffic estimation errors. The prediction confidence is quantified by exploiting the proposed prediction region analysis.
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Accurate traffic prediction is important for efficient traffic operation, management, and user convenience. It enables traffic management authorities to allocate traffic resources efficiently, reducing traffic congestion and minim...
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Accurate traffic prediction is important for efficient traffic operation, management, and user convenience. It enables traffic management authorities to allocate traffic resources efficiently, reducing traffic congestion and minimizing travel time for commuters. With the increase in data sources, traffic prediction methods have shifted from traditional model-based approaches to more data-driven methods. However, accurately predicting traffic under unforeseen events, such as crashes, adverse weather conditions, and road works, remains a challenging task. Hybrid traffic prediction models that combine data-driven and model-based approaches have emerged as promising solutions, considering the advantage of the model-based approach that can replicate unobserved scenarios. This paper proposes a hybrid traffic prediction framework named SMURP (Simulation and Machine-learning Utilization for Real-time Prediction), which overcomes the limitations of the existing methods. The SMURP is a framework that can be applied to any data-driven prediction method. When an event is detected during prediction, the SMURP complements the prediction outcomes with an additional predictor that uses simulated traffic data. The proposed framework is applied to various data-driven prediction models and evaluated in the actual road section. The results show that applying the SMURP to data-driven prediction methods can improve prediction accuracy.
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Traffic prediction is critical for the success of intelligent transportation systems (ITS). However, most spatio-temporal models suffer from high mathematical complexity and low tune-up flexibility. This article presents a novel s...
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Traffic prediction is critical for the success of intelligent transportation systems (ITS). However, most spatio-temporal models suffer from high mathematical complexity and low tune-up flexibility. This article presents a novel spatio-temporal random effects (STRE) model that has a reduced computational complexity due to mathematical dimension reduction, with additional tune-up flexibility provided by a basis function capable of taking traffic patterns into account. Bellevue, WA, was selected as the model test site due to its widespread deployment of loop detectors. Data collected during the 2 weeks of July 2007 from 105 detectors in the downtown area were used in the modeling process and traffic volumes predicted for 14 detectors for the entire month of July 2008. The results show that the STRE model not only effectively predicts traffic volume but also outperforms three well-established volume prediction models, the enhanced versions of autoregressive moving average (ARMA) and spatiotemporal ARMA, and artificial neural network. Even without further model tuning, all the experimental links produced mean absolute percentage errors between 8% and 16% except for three atypical locations. Based on lessons learned, recommendations are provided for future applications and tune-up of the proposed STRE model.
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Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic ...
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Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic status using past average traveler speed (ATS). Five different algorithms are proposed for predicting the traffic status. They are applied to real data provided by the Traffic Control Center of Istanbul Metropolitan Municipality. Algorithm 1 predicts future ATS on a highway section based on the past speed information obtained from the same road section. The other proposed algorithms, Algorithms 2 through 5, predict the traffic status as fluent, moderately congested, or congested, again using past traffic state information for the same road segment. Here, traffic states are assigned according to predetermined intervals of ATS values. In the proposed algorithms, ATS values belonging to past five consecutive 10-minute time intervals are used as input data. Performances of the proposed algorithms are evaluated in terms of root mean square error (RMSE), sample accuracy, balanced accuracy, and processing time. Although the proposed algorithms are relatively simple and require only past speed values, they provide fairly reliable results with noticeably low prediction errors.
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Dynamic traffic management (DTM) is the management of traffic streams and of the demand for traffic. Real-time data are essential for correct information to drivers and for control of traffic. Not every variable can be measured di...
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Dynamic traffic management (DTM) is the management of traffic streams and of the demand for traffic. Real-time data are essential for correct information to drivers and for control of traffic. Not every variable can be measured directly, so models are necessary to calculate missing information or to predict the future state of the network. This paper follows a top down approach. It starts with the policy context. The different DTM-tools considered are grouped according to their effect either on travel demand, modal split or a specific travel mode. Some recent developments are watched with respect to transport research laboratories. We argue that an emphasis should be laid upon a neat development architecture. For this reason we will pay attention to the traffic model FLEXSYT The strong point of this model is the use of a traffic control language which allows for the investigation of any type of control for any type of network. Finally we discuss predictability. It is important to identify what can be predicted reasonably and what not.
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Road traffic crashes are a considerable concern in motorized countries because of their impact on society, economy. The number of accidents has decreased since 2000. This paper gives an overview of road safety on Hungary's road ne...
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Road traffic crashes are a considerable concern in motorized countries because of their impact on society, economy. The number of accidents has decreased since 2000. This paper gives an overview of road safety on Hungary's road network and reviews the efforts that were done. Statistical prediction of road traffic crashes is described based on the findings of the scientific literature. Using the data provided by the Hungarian Central Statistical Office for the number of crashes and related factors for the period from 2002 to 2017, a new relationship is established between crashes and a number of related factors in an attempt to improve the models' prediction power and to investigate the effect of adding new predictors on the strength of the models.
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Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved t...
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Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN-based approaches proposed for short-term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in-depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short-term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks. This article is categorized under:
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Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged ...
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Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.
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In this paper, we propose a detrending based and deep learning based many-to-many traffic prediction model called DeepTrend 2.0 that accepts information collected from multiple sensors as input and simultaneously generates the pre...
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In this paper, we propose a detrending based and deep learning based many-to-many traffic prediction model called DeepTrend 2.0 that accepts information collected from multiple sensors as input and simultaneously generates the prediction for all the sensors as output. First, we demonstrate that detrending brings advantages to traffic prediction, even when deep learning models are considered. Second, the proposed model strikes a delicate balance between model complexity and accuracy. In contrast to the existing models that view a sensor network as a weighted graph and use graph convolutional neural networks (GCNN) to model spatial dependency, we represent a sensor network as an image and propose a convolutional neural network (CNN) as the prediction model. The image is generated by the correlation coefficient between the flow series of sensors, which is different from other CNN based prediction approaches that convert the transportation network into an image by the spatial location of sensors or regions. Compared with the GCNN based model, the CNN based DeepTrend 2.0 can achieve much faster convergence during training, and it guarantees similar prediction quality. Test results indicate that the proposed light-weighted model is efficient and easy to transfer and deploy.
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Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. Fo...
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Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion.
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