A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

Tedjopurnomo, David Alexander; Bao, Zhifeng; Zheng, Baihua; Choudhury, Farhana Murtaza; Qin, A. K.

doi:10.1109/tkde.2020.3001195

Cited by 206 publications

(92 citation statements)

References 66 publications

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“…Difference with Existing Surveys. Although there are some surveys [1,[11][12][13][14][15][16][17][18][19], they only focused on some aspects of traffic prediction, but did not give a complete survey and did not cover most recent works. At first, Wang et al [18] only survey the management and analytics of trajectories, which is one kind of spatial dynamic temporal dynamic data, so they lack the discussion on other kinds of spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, many other surveys just focus on one special kind of traffic prediction problems. For example, Tang et al [19] focus on the methodology review about the clearance time prediction of road incidents, while the authors in [1,11,16] just focus on surveying the traffic flow prediction using machine learning methods. Hence, they cannot give a broad review on the whole domain of traffic prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

217

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

217

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“…The research carried out by [48][49][50][51] summarized the applicable models that depend on conventional techniques and some early deep learning techniques. Alexander et al [52] outlined a comprehensive survey of deep neural networks to predict the traffic flow of vehicles. Their research discussed three well-known deep neural architectures comprising convolutional, recurrent, and feed-forward neural networks.…”

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confidence: 99%

“…Their research discussed three well-known deep neural architectures comprising convolutional, recurrent, and feed-forward neural networks. However, some recent technological innovations involving graph-based deep learning were not discussed in their research [52]. Likewise, researchers such as [53] investigated a well-detailed survey of graph-based deep learning architecture, including their applications in the field of traffic flow.…”

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confidence: 99%

Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

et al. 2021

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The accurate and effective prediction of the traffic flow of vehicles plays a significant role in the construction and planning of signalized road intersections. The application of artificially intelligent predictive models in the prediction of the performance of traffic flow has yielded positive results. However, much uncertainty still exists in the determination of which artificial intelligence methods effectively resolve traffic congestion issues, especially from the perspective of the traffic flow of vehicles at a four-way signalized road intersection. A hybrid algorithm, an artificial neural network trained by a particle swarm optimization model (ANN-PSO), and a heuristic Artificial Neural Network model (ANN) were compared in the prediction of the flow of traffic of vehicles using the South Africa transportation system as a case study. Two hundred and fifty-nine (259) traffic datasets were obtained from the South African road network using inductive loop detectors, video cameras, and GPS-controlled equipment. For the ANN and ANN-PSO training and testing, 219 traffic data were used for the training, and 40 were used for the testing of the ANN-PSO model, while training (160), testing (40), and validation (59) was used for the ANN. The ANN result presented a logistic sigmoid transfer function with a 13–6–1 model and a testing R2 of 0.99169 compared to the ANN-PSO result, which showed a testing performance of R2 0.99710. This result shows that the ANN-PSO model is more efficient and effective than the ANN model in the prediction of the traffic flow of vehicles at a four-way signalized road intersection. Furthermore, the ANN and ANN-PSO models are robust enough to predict traffic flow due to their better testing performance. The modelling approaches proposed in this study will assist transportation engineers and urban planners in designing a traffic control system for traffic lights at four-way signalized road intersections. Finally, the results of this research will assist transportation engineers and traffic controllers in providing traffic flow information and travel guidance for motorists and pedestrians in the optimization of their travel time decision-making.

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“…Some describe them as a blackbox approach, and adequately so. Even when weights and feature maps are on hand, the predictions made by a neural network are not easily interpretable in terms of logic [89]. Ideally, a user should be able to tell which features do the filters capture or the logic behind selecting these filters.…”

Section: Interpretabilitymentioning

confidence: 99%

Deep Learning-based Image Analysis for High-content Screening

Zeng¹

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High-content screening is an empirical strategy in drug discovery toidentify substances capable of altering cellular phenotype — the set ofobservable characteristics of a cell — in a desired way. Throughout thepast two decades, high-content screening has gathered significant attentionfrom academia and the pharmaceutical industry. However, imageanalysis remains a considerable hindrance to the widespread applicationof high-content screening. Standard image analysis relies on feature engineeringand suffers from inherent drawbacks such as the dependence onannotated inputs. There is an urging need for reliable and more efficientmethods to cope with increasingly large amounts of data produced. This thesis centres around the design and implementation of a deeplearning-based image analysis pipeline for high-content screening. Theend goal is to identify and cluster hit compounds that significantly alterthe phenotype of a cell. The proposed pipeline replaces feature engineeringwith a k-nearest neighbour-based similarity analysis. In addition, featureextraction using convolutional autoencoders is applied to reduce thenegative effects of noise on hit selection. As a result, the feature engineeringprocess is circumvented. A novel similarity measure is developed tofacilitate similarity analysis. Moreover, we combine deep learning withstatistical modelling to achieve optimal results. Preliminary explorationssuggest that the choice of hyperparameters have a direct impact on neuralnetwork performance. Generalised estimating equation models are usedto predict the most suitable neural network architecture for the input data. Using the proposed pipeline, we analyse an extensive set of images acquiredfrom a series of cell-based assays examining the effect of 282 FDAapproved drugs. The analysis of this data set produces a shortlist of drugsthat can significantly alter a cell’s phenotype, then further identifies fiveclusters of the shortlisted drugs. The clustering results present groups ofexisting drugs that have the potential to be repurposed for new therapeuticuses. Furthermore, our findings align with published studies. Comparedwith other neural networks, the image analysis pipeline proposedin this thesis provides reliable and better results in a shorter time frame.

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

Cited by 206 publications

References 66 publications

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

Deep Learning-based Image Analysis for High-content Screening

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