Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway

Zang, Di; Ling, Jiawei; Cheng, Jiujun; Tang, Keshuang; Li, Xin

doi:10.1007/978-3-319-68121-4_22

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Convolutional neural networks (CNN) have also been used to understand traffic flow patterns. Both temporal and spatial features have been used to generate twodimensional feature sets that can be exploited with CNN (37)(38)(39). Several extensions of CNN were also reported to have superior accuracy to traditional CNNs, such as eRCNN (40) and GraphCNN (41,42).…”

Section: Assessment Of Traffic-related Prediction Using Deep Learningmentioning

confidence: 99%

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Islam

Abdel-Aty

Ugan

2023

Transportation Research Record: Journal of the Transportation R

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Signal phasing and timing can be adaptive and actuated in practice. This makes it challenging to understand what the cycle length and phase duration of the next few cycles will be. Many innovative applications can be designed based on the knowledge of future signal timing states such as dilemma zone warning, efficient route planning, and so forth. This work proposes a long short-term memory model capable of predicting both cycle length and phase duration prediction up to six cycles in the future. GPS information of several vehicles are merged with signal timing information of eight intersections. Several key features such as waiting time, approach speed and acceleration, departing speed and acceleration, are calculated based on the geolocation of individual journeys. The results show that cycle length prediction can reach mean absolute error (MAE) of about 7 s while phase prediction MAE is about 9 s.

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Section: Assessment Of Traffic-related Prediction Using Deep Learningmentioning

confidence: 99%

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Islam

Abdel-Aty

Ugan

2023

Transportation Research Record: Journal of the Transportation R

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“…A CNN model was first built by Ma et al to predict the traffic speed in a traffic network based on transformed grayscale images [27]. Zang et al then proposed a CNN model with asymmetric convolution kernels, aiming to exploit the potential for learning spatiotemporal features [28]. An improved model using geometric algebra convolution was established by Zang et al named geometric algebra residual neural network to fully discover the relationship between traffic parameters when making prediction [29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zang et al. then proposed a CNN model with asymmetric convolution kernels, aiming to exploit the potential for learning spatiotemporal features [28]. An improved model using geometric algebra convolution was established by Zang et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Lane‐level short‐term travel speed prediction for urban expressways: An attentive spatio‐temporal deep learning approach

Tang,

Chen,

Cao

et al. 2023

IET Intelligent Trans Sys

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Numerous efforts have been made to address the section‐level travel speed prediction problem. However, section‐level predictions can hardly be used for fine‐grained applications, such as lane management and lane‐level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three‐dimensional (3D) dual attention convolution‐based deep learning model for predicting the lane‐level travel speed. 3D convolutions are designed to learn high‐dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio‐temporal learning ability, based on targeting the lane‐level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in‐depth analysis is provided regarding the attention coefficients and interpretation of real‐world lane‐level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane‐level traffic flow.

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“…Consequently, all the convolutions performed by the CNN proposed in [34] are 1-dimensional convolutions. The studies presented in [35], [36] and [37] propose utilizing 2-dimensional convolutions so that CNNs can simultaneously process the temporal and spatial evolution of the traffic variables. To this aim, traffic data is represented as a matrix where one direction represents the spatial evolution of the data and the other one the temporal evolution.…”

Section: State Of the Art On Traffic Predictionmentioning

confidence: 99%

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mena-Oreja

Gozálvez

2020

IEEE Access

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Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

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Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway

Cited by 8 publications

References 19 publications

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Lane‐level short‐term travel speed prediction for urban expressways: An attentive spatio‐temporal deep learning approach

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

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