A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

Wang, Haizhong; Liu, Lu; Dong, Shangjia; Qian, Sean; Wei, Heng

doi:10.1080/21680566.2015.1060582

Cited by 73 publications

(53 citation statements)

References 35 publications

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“…Traditional traffic flow prediction models include parametric models such as Autoregressive Integrated Moving Average model (ARIMA) [7], Kalman filtering model [8] and their extensions. For example, EMD-ARIMA model [9], Seasonal ARIMA [10], an integration of Kalman filter and ARIMA [11] have been reported for traffic flow prediction in literature. These time series prediction methods have been developed to assist Transportation Cyber-Physical Systems (TCPS) to analyze traffic flow and predict the traffic trend.…”

Section: A Traditional Machine Learningmentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively-available traffic data collected from various sensors in Transportation Cyber-Physical Systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, we propose a wide-attention and deep-composite (WADC) model consisting of a wide-attention module and a deep-composite module in this paper. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with selfattention mechanism. The deep-composite module can generalize local key features via Convolutional Neural Network component and Long Short-Term Memory Network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

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Section: A Traditional Machine Learningmentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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“…One existing monocular camera-based method to vehicle speed estimation utilizes the technique of Camera Calibration [1,5,6]. In short, this method obtains the algorithm-generated scale and calculates the speed based on the vehicle trajectories acquired by a two-stage process including detection and tracking.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

2019

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Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial–temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62 .

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“…In recent years, developed countries are maintaining and reconstructing aged traffic facilities and developing countries are constructing more new traffic facilities, which both bring a large number of work zones on city arterials and freeways. Work zones can disrupt the normal traffic flow and cause many traffic problems [1][2][3][4][5]. Studies have displayed that work zones can reduce road capacity [6][7][8][9] and increase traffic delay and traffic accident [1,[10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Model for the capacity of the urban signal intersection with work zone

Yang

Zhu

et al. 2016

J of Advced Transportation

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Summary Work zones exist widely on urban arterials in the cities that are undergoing road construction or maintenance. However, the existing studies on arterial work zones are very limited, especially on the work zones at urban intersections, although they have a severe negative impact on the urban traffic system. For the first time, this study focuses on how work zones reduce intersection capacity. A type of widely observed work zone, the straddling work zone that straddles on a road segment and an intersection, is studied. A linear regression model and a multiplicative model suggested by Highway Capacity Manual are proposed respectively to determine the saturation flow rate of the signal intersection with the straddling work zone. The data of 22 straddling work zones are collected and used to evaluate the performances of the proposed models. The results display that the linear regression model outperforms the multiplicative model suggested by Highway Capacity Manual. The study also reveals that reducing approach (or exit) lanes and the mixture of motor vehicles and non‐motor vehicles (and pedestrians) can significantly decrease the capacity of the intersection with straddling work zone. Therefore, in setting a straddling work zone, workers should try to ensure that the intersection approach and exit are unobstructed and set a separation for non‐motors and pedestrians to avoid mixed traffic flow. Copyright © 2016 John Wiley & Sons, Ltd.

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A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

Cited by 73 publications

References 35 publications

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

Model for the capacity of the urban signal intersection with work zone

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