Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data

Islam, Zubayer; Abdel-Aty, Mohamed; Mahmoud, Nada

doi:10.1016/j.trc.2022.103742

Cited by 17 publications

(13 citation statements)

References 52 publications

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“…CNN models consist of an input layer, convolutional layer, pooling layer and output layer. Local perception and weight sharing can be achieved through convolutional processing [41]. CNNs are mainly employed to obtain the spatial features of traffic flow data.…”

Section: Implementation Of the Cnn Modelmentioning

confidence: 99%

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Bing,

Zhao,

Ren

et al. 2024

Sustainability

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Because of the random volatility of traffic data, short-term traffic flow forecasting has always been a problem that needs to be further researched. We developed a short-term traffic flow forecasting approach by applying a secondary decomposition strategy and CNN–Transformer model. Firstly, traffic flow data are decomposed by using a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Secondly, the IMF1 obtained from the CEEMDAN is further decomposed into some sub-series by using Variational Mode Decomposition (VMD) algorithm. Thirdly, the CNN–Transformer model is established for each IMF separately. The CNN model is employed to extract local spatial features, and then the Transformer model utilizes these features for global modeling and long-term relationship modeling. Finally, we obtain the final results by superimposing the forecasting results of each IMF component. The measured traffic flow dataset of urban expressways was used for experimental verification. The experimental results reveal the following: (1) The forecasting performance achieves remarkable improvement when considering secondary decomposition. Compared with the VMD-CNN–Transformer, the CEEMDAN-VMD-CNN–Transformer method declined by 25.84%, 23.15% and 22.38% in three-step-ahead forecasting in terms of MAPE. (2) It has been proven that our proposed CNN–Transformer model could achieve more outstanding forecasting performance. Compared with the CEEMDAN-VMD-CNN, the CEEMDAN-VMD-CNN–Transformer method declined by 13.58%, 11.88% and 11.10% in three-step-ahead forecasting in terms of MAPE.

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Section: Implementation Of the Cnn Modelmentioning

confidence: 99%

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Bing,

Zhao,

Ren

et al. 2024

Sustainability

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“…As such, it is evident that SPaT predictions at a corridor level using both adaptive and actuated signal control have not yet been studied. SPaT predictions have been explored by Islam et al ( 26 ) using detector data but not with connected vehicle data.…”

Section: Literature Reviewmentioning

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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“…The main limitation of all the studies is that crash events are usually rare and therefore, these studies would only rely on the spatial relationship between crash events and traffic parameters. It has been shown in several studies that the temporal relationship need to be included as well since traffic parameters and signal timing would vary largely throughout the day and even across days 14 – 16 . Moreover, there are notable shortcomings of these types of police reported crash data such as incorrect reasoning, subjectivism, inaccurate data, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of signal timing on vehicles’ near misses at intersections

Islam

Abdel-Aty

Goswamy

et al. 2023

Sci Rep

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Driving characteristics often vary between the different states of the signal. During red and yellow phase, drivers tend to speed up and reduce the following distance which in turn increases the possibility of rear end crashes. Intersection safety, therefore, relies on the correct modelling of signal phasing and timing parameters, and how drivers respond to its changes. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned aerial vehicle (UAV) video data has been used to study a major intersection. Post encroachment time (PET) between vehicles was calculated from the video data as well as speed, heading and relevant signal timing parameters such as all red time, red clearance time, yellow time, etc. Random parameter ordered logit model was used to model the relationship between PET and signal timing parameters. Overall, the results showed that yellow time and red clearance time is positively related to PETs. The model was also able to identify certain signal phases that could be a potential safety hazard and would need to be retimed by considering the PETs. The odds ratios from the models also indicate that increasing the mean yellow and red clearance times by one second can improve the PET levels by 10% and 3%, respectively.

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Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data

Cited by 17 publications

References 52 publications

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Effect of signal timing on vehicles’ near misses at intersections

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