Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections

Li, Bing; Cheng, Wei; Li, Lishan

doi:10.1155/2018/5020518

Cited by 15 publications

(6 citation statements)

References 51 publications

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“…e existing traffic flow prediction models are divided into traditional traffic flow prediction models and traffic flow prediction models based on Machine Learning. e commonly used traditional flow prediction models include the Historical Average Model (HAM) [18], Kalman Filtering Model (KFM) [10,11], and Autoregressive Integrated Moving Average Model (ARIMA) [12,13]. HAM takes the average data of historical traffic flow as the result and the calculation is simple and efficient.…”

Section: Related Workmentioning

confidence: 99%

“…e traditional traffic flow prediction method [8,9] is to predict the future traffic flow by considering the time correlation of traffic flow data and learning the data characteristics of historical traffic flow, such as Kalman filtering model (KFM) [10,11], Autoregressive Integrated Moving Average (ARIMA) model [12,13], k-nearest neighbor model [14,15], Bayesian model [16,17], and so on. ese methods consider the dynamic changes of traffic conditions with time but ignore the influence of space, so they can not accurately predict traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

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Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Jiang

Xiao

Liu

et al. 2022

Journal of Advanced Transportation

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Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Jiang

Xiao

Liu

et al. 2022

Journal of Advanced Transportation

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show abstract

“…In [26], the authors applied the Lighthill-Whitham-Richards shockwave theory and Robertson's platoon dispersion model to predict the arrival of vehicles in advance at intervals of 5 seconds. This study did not consider vehicle types and did not derive the discharging times.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning

Chen

Lin

Yeh

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection's queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles. INDEX TERMS Deep learning (DL), traffic queue dissipation time, traffic queue pattern, mixed traffic flows, object detection, traffic signal countdown timer (TSCT).

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“…The authors achieved an average RMSE of 2.33 vehicles, MAE of 1.82 vehicles, and MAPE of 16.12% for maximum queue length prediction. However, this model does not consider several aspects of real-world traffic flow that affect queue lengths, such as lane changing, heterogeneous traffic and dynamic correction of travel times (Li et al (2018)). Zeng et al developed a queue length prediction model using stochastic fluid theory.…”

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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Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections

Cited by 15 publications

References 51 publications

Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning

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

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