Research on the Queue Length Prediction Model with Consideration for Stochastic Fluid

Zeng, Xiaoqing; Zhan, Jifei; Lee, Yang; Xiong, Qipeng; Chen, Yujia

doi:10.1007/978-981-10-3575-3_6

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…The average relative prediction error of the model is 24.7% for single lane scenario and 38.2% for multilane scenario. This model also struggles with multilane scenario due to the existence of lane changing (Zeng et al (2017)).…”

Section: Queue Length Predictionmentioning

confidence: 99%

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Comert¹,

Khan²,

Rahman³

et al. 2019

Preprint

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Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time in order to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on the many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using four variations of Grey systems: (i) the first order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections (EGVM). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, and long short-term memory (LSTM) neural network. Based on our analyses, we found that EGVM reduces the prediction error over closest competing models (i.e., LSTM and Additive Autoregressive (AAR) time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error (RMSE), and 51% and 50%, respectively, in terms of Mean Absolute Error (MAE).

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Section: Queue Length Predictionmentioning

confidence: 99%

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Comert¹,

Khan²,

Rahman³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Yang et al [23] developed a platoon cooperation strategy based on the formation process from single vehicle to coordination platoon, and observed a clear increase in road capacity under the platoon scenario. Zeng et al [24] proves the equivalent queue length prediction models can quantitatively describe the existence of stochastic traffic fluid in roads. Rahman et al [25] utilizes the queue lengths at two upstream intersections and the current intersection to conduct real-time prediction of the queue length.The aforementioned studies indicate that deep learning can be applied to short-term traffic flow prediction and has achieved certain results.…”

Section: Introductionmentioning

confidence: 99%

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Chen

Guo

et al. 2023

IEEE Access

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In response to the problem of fixed time intervals for short-term traffic flow prediction, which fails to meet the requirements of traffic signal control based on traffic cycle signals, this paper proposes an improved long short-term memory-based method for periodic traffic volume prediction. The method presented in this study involves improvements to the Long Short-Term Memory (iLSTM) and Bidirectional Long Short-Term Memory (iBiLSTM) models, leading to the construction of the iBiLSTM-iLSTM -NN model. This model incorporates spatial data from surrounding intersections and employs data fitting techniques to establish the correlation between periodic queue length and traffic volume. Subsequently, a predictive model for periodic traffic volume is developed based on this correlation, enabling reliable forecasting of future traffic volumes within a given cycle. Additionally, actual intersection data is collected for simulation analysis. The results indicate that the prediction error of periodic traffic volume is influenced by different traffic flow characteristics such as peak, off-peak, and normal periods, as well as different inbound lanes. Different model parameters have a noticeable impact on the model's performance, with smaller batch sizes leading to more stable models. By comparing the performance of different prediction models using various error evaluation metrics, this study finds that the proposed model exhibits the most stable performance. The research findings can be applied to rapidly predict future traffic volumes for several periods based on the instantaneous queue length at the end of the red signal phase, providing reliable, accurate, and timely data for urban traffic signal control.

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Study on Optimal Model of Traffic Signal Control at Oversaturated Intersection

Zeng

Zhan

et al. 2019

Smart Innovation, Systems and Technologies

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Research on the Queue Length Prediction Model with Consideration for Stochastic Fluid

Cited by 5 publications

References 2 publications

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Study on Optimal Model of Traffic Signal Control at Oversaturated Intersection

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