Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features

Li, Tao; Ni, Anning; Zhang, Chunqin; Xiao, Guangnian; Gao, Linjie

doi:10.1049/iet-its.2020.0406

Cited by 32 publications

(19 citation statements)

References 45 publications

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“…Equivalence proof of lower-level model LP (1) We use the first-order optimality conditions of model LP (1) to demonstrate its equivalence to the UE principle and the logitbased trip distribution. According to the optimum theory, the solution of the mathematical programming satisfies its firstorder optimality conditions.…”

Section: 22mentioning

confidence: 99%

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Modelling the road network capacity considering residual queues and connected automated vehicles

Zhang

Yue

Shao

et al. 2021

IET Intelligent Trans Sys

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This paper investigates the network capacity considering residual queues and connected automated vehicles. First, the weighted network outflow is introduced to measure the network capacity. Then, two bilevel models are built to calculate the network capacity with residual queues under the user equilibrium (UE) principle and the system optimum (SO) principle, respectively. The UE‐based network capacity model is employed to estimate the capacity of the dedicated road network for human‐driven vehicles (HVs), while the SO‐based network capacity model is employed to estimate the capacity of the dedicated road network for connected automated vehicles (CAVs). Furthermore, by combining the two proposed network capacity models, a model to allocate link capacity between CAV lanes and HV lanes to maximise the whole network capacity is presented, in which the CAV travellers’choice behaviour of driving modes is considered. Finally, through two numerical examples, the characteristics of the proposed models are demonstrated and their potential applications are discussed. The results show that the proposed models are capable of 1) identifying critical links and potential critical links of networks, 2) guiding the land‐use development and macroscopic travel demand management, 3) evaluating network optimisation schemes, and 4) allocating the road supply to CAVs and HVs efficiently.

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Section: 22mentioning

confidence: 99%

“…Traffic congestion, which has been a serious issue in metropolises across the world [1], brings massive inconveniences to people and huge losses to the economy [2]. In the development process of smart cities, traffic congestion will be an intractable problem, which needs to be addressed urgently [3].…”

Section: Introductionmentioning

confidence: 99%

Modelling the road network capacity considering residual queues and connected automated vehicles

Zhang

Yue

Shao

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

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“…For instance, considering the adaptability and periodic characteristics of short-term traffic flow, the seasonal ARIMA model (SARIMA) [10]and an adaptive ARIMA model [11] are proposed; Zhang et al, propose an adaptive KF model to improve the effect of traffic flow prediction. In addition, the research of artificial intelligence models (AI-based models) is a hot spot in recent years, such as the long short-term memory (LSTM) model, convolutional neural network (CNN) [15][16][17]. But they need to preset the parameters to learn the training samples.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

Zhao

Wen

Wang

et al. 2021

IET Intelligent Trans Sys

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The linearity and heteroscedasticity are the important characteristics of short-term traffic flow data. Generally, the autoregressive integrated moving average (ARIMA) model and the generalized autoregressive conditional heteroscedasticity (GARCH) model are respectively used to explain these characteristics. However, the prerequisite for the use of ARIMA is that the training residuals should follow the standard Gaussian distribution, which is hard to be satisfied in practice. Meanwhile, the variance in the GARCH model usually neglects the time-varying characteristic. To address these problems, this paper proposes an innovative method based on the combination of ARIMA, maximum correntropy criterion (MCC), conditional kernel density estimation (CKDE), and GARCH. Specifically, the MCC method is first employed to estimate the coefficients of the ARIMA model (i.e. ARIMA-MCC), by which the linear prediction is conducted. Then, the CKDE model is established to describe the training residuals obtained by ARIMA-MCC and estimate the time-varying variance in the GARCH model (i.e. GARCH-CKDE). Case studies based on four groups of data measured in the urban road are used to evaluate the performance of the proposed method. Compared with the traditional ARIMA model, the improvement by the proposed method reaches 14.45% in terms of the evaluation criterion of mean absolute percentage error.

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“…The training process sets the base learning rate β to 0.005 and the dropout value is 0.5. Using the Adam iterative optimization algorithm [50], the learning rate of each parameter is dynamically adjusted using first-order moment estimation and second-order moment estimation of the gradient with the following equations.…”

Section: Model Trainingmentioning

confidence: 99%

Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Chen

Xie

et al. 2021

Energies

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With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

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Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features

Cited by 32 publications

References 45 publications

Modelling the road network capacity considering residual queues and connected automated vehicles

Modelling the road network capacity considering residual queues and connected automated vehicles

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

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