Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm

Tang, Jinjun; Zeng, Jie; Wang, Yuwei; Yuan, Hang; Liu, Fang; Huang, Helai

doi:10.1080/23249935.2020.1845250

Cited by 31 publications

(11 citation statements)

References 38 publications

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“…Tang et.al. 16 proposed an attention-based long short-term memory genetic algorithm (GA-LSTM), which combined spatiotemporal correlation analysis to predict urban road traffic flow. Messaoud et.al.…”

Section: Related Workmentioning

confidence: 99%

Attention-LSTM Based Prediction Model for Aircraft 4-D Trajectory

Jia

Chen

Zhang

et al. 2022

Preprint

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With the increase of aviation activities, airspace constraints and flight delays have become increasingly prominent in the process of air traffic management (ATM). How to increase airspace capacity within the limited airspace resources while ensuring smooth and safe aircraft operations is a challenge for civil aviation today. In order to accelerate the intelligent operation of air traffic control and promote the development of intelligent ATM systems, it becomes very important to improve the accuracy of trajectory prediction, which becomes very difficult due to the problems of sparse aircraft flight trajectory and different flight altitudes. In order to solve this problem, an Attention-LSTM trajectory prediction model is proposed in this paper. This model can extract the time series features of the trajectory using an long short-term memory (LSTM) network and improve the accuracy of trajectory prediction by extracting the important factors affecting the current point change using an attention mechanism. To verify the accuracy of our proposed model, this paper uses real trajectory data, and uses LSTM, support vector machine, and other models for comparison. The comparison results show that this model is better than the most advanced model, which further promotes the process of intelligent ATM systems.

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Section: Related Workmentioning

confidence: 99%

Attention-LSTM Based Prediction Model for Aircraft 4-D Trajectory

Jia

Chen

Zhang

et al. 2022

Preprint

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“…e gaps need to be filled are listed as follows: (1) the research area is small, and a large number of studies mainly focus on the ramp area of expressways, lacking the research on long-distance trunk lines; (2) the simulation data are mainly based on historical traffic volume, and lack of prediction of future traffic volume changes, resulting in low reference of simulation results. At present, the research on traffic flow modeling and prediction has also made great progress [27,28], but it is rarely applied to VSL; (3) a large number of studies start from the microperspective, but there is lack of analysis from the macro perspective. In view of the abovementioned problems, this paper selects the expressway trunk line as the main research area from a macro point of view and introduces the traffic flow prediction into the control strategy before the implementation, so as to predict the future traffic changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Variable Speed Limit Strategies Based on the Macro Hierarchical Control Traffic Flow Model

Wang

Ren

et al. 2021

Journal of Advanced Transportation

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The superior traffic control system can promote the efficiency of mainstream expressway. As the effective method to smooth traffic system, the variable speed limits (VSL) strategies are discussed in an expressway traffic network. The dynamic OD estimation model is used to produce the real traffic information, which is loaded to the traffic network. Then, the prediction information of traffic variables and the VSL strategy are introduced to macro hierarchical control traffic flow model. A solution algorithm is further developed to find the optimal parameters of VSL by minimizing the total travel time and delay. The simulation results show that the proposed strategy perfects well, the traffic congestion is effectively alleviated, and the traffic efficiency of the road section is significantly improved. This framework can be adopted by transit managers for traffic efficiency.

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“…The training dataset is fed to a feed-forward neural network with 6 layers; a single input layer with a size 1600 representing the size of the input image after resizing, four hidden layers in different sizes and finally an output layer with one activation unit. The output activation unit represents whether a character is recognized (1) or not (0). To reduce the non-linearity of the output of neurons, the sigmoid activation function is used.…”

Section: Character/non-character Classifiermentioning

confidence: 99%

“…The rapid growth in the number of vehicles has led to the continuous need for more use of the Intelligent Transportation System (ITS) to address many security-and trafficmanagement challenges, including finding stolen cars, banning violations, managing parking lots, monitoring cars at traffic lights, and others. The ITSs that aim to make the use of transportation networks safer and smarter have been benefiting from recent advances in image processing and machine intelligence techniques towards the development of more intelligent roads, vehicles, and users [1,2]. License plate recognition (LPR) is typically one of the key components of the ITS.…”

Section: Introductionmentioning

confidence: 99%

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

et al. 2021

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The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.

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Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm

Cited by 31 publications

References 38 publications

Attention-LSTM Based Prediction Model for Aircraft 4-D Trajectory

Attention-LSTM Based Prediction Model for Aircraft 4-D Trajectory

Variable Speed Limit Strategies Based on the Macro Hierarchical Control Traffic Flow Model

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

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