A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Zhao, Liang; Liu, Yufei; Al‐Dubai, Ahmed; Zomaya, Albert Y.; Min, Geyong; Hawbani, Ammar

doi:10.1109/jiot.2020.3021141

Cited by 81 publications

(21 citation statements)

References 29 publications

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“…ey consider both spatial dependency and temporal property and employ the residual neural network framework to dynamically aggregate them to predict the final traffic of crowds. ere are also some works that focus on vehicular network to prediction future location of vehicle [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

Feng

Zhang

et al. 2022

Journal of Advanced Transportation

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With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω-region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω-regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω-regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance.

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Section: Related Workmentioning

confidence: 99%

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

Feng

Zhang

et al. 2022

Journal of Advanced Transportation

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“…Traffic signal control in vehicular networks enables traffic signal controllers at intersections to manage and control traffic, such as selecting traffic phases (e.g., the north-south and west-east bounds) following traffic rules (e.g., vehicles cannot make a right turn) for reducing congestion at intersections [57,58]. In [33], traffic signal controllers select their respective traffic phases in a distributed manner.…”

Section: Applicationsmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

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Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

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“…LSTMs were proposed in [39] to predict the vehicular trajectories in highways. GANs were used in [19] to predict vehicular trajectory position and speed in urban scenarios. The adoption of different neural network models combined in a single architecture has also been used to address mobility prediction, as in [16], where a LSTM model is combined with a convolutional one to forecast the most likely potential passenger for taxi drivers.…”

Section: Related Workmentioning

confidence: 99%

“…The prediction of vehicular trajectories is usually based on prior vehicular data, particularly the latest visited positions, as considered in [1], [15]- [19]. The prediction is useful for several purposes, ranging from the computation of the estimated travel time of a given route, the support for route planning or the computation of vehicular staying time at particular locations [20], [21].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

Irio

Oliveira

2021

IEEE Open J. Veh. Technol.

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This work compares two innovative methodologies to predict the future locations of moving vehicles when their current and previous locations are known. The two methodologies are based on: (a) a Bayesian network model used to infer the statistics of prior vehicles' trajectory data that is further adopted in the estimation process; (b) a deep learning approach based on recurrent neural networks (RNNs), more specifically Long Short-term Memory (LSTM) networks. Both methodologies are evaluated with GPS traces of multiple taxis servicing Porto city in Portugal. After a characterization of the dataset, we present experimental results obtained with both prediction methodologies. The results indicate that the prediction accuracy is improved in both methods as more information about prior vehicle mobility is available. The Bayesian network-based method is advantageous because the statistical inference can be updated in real-time as more trajectory data is known. On the contrary, the RNN-based method requires a time-consuming learning task every time new data is added to the inference dataset. However, the RNN achieves a higher prediction accuracy performance (3% to 5% higher) than the Bayesian method. Additionally, we show that the computational cost to predict the next position a vehicle will move to can be substantially reduced when the Bayesian network is adopted, a scenario where the RNN method requires more computational time. But when the quantity of prior data used in the prediction increases, the computational time required by the RNN-based method can be two orders of magnitude lower than the Bayesian network-based approach, showing that the RNN method is advantageous in both accuracy and computational time. Finally, we highlight that both methods achieve a next position successful prediction rate higher than 90%, confirming the applicability and validity of the proposed methods.

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A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Cited by 81 publications

References 29 publications

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

A New Approach to Regional Traffic Estimation for Intelligent Transport Systems

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

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