Review on Deep Learning in Intelligent Transportation Systems

Liu, Yiwei; Zhang, Yizhuo; Chen, Chi‐Hua

doi:10.1007/978-981-15-8462-6_45

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…For example, Challita et al [23] proposed a deep reinforcement learning based method for a cellular UAV network by optimizing the 2D path and cell association to achieve a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference on the path. A similar scheme is applied to provide intelligent traffic light control in [24].…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Zhu¹,

Liu²,

Wang³

2019

Preprint

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Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission control (power and channel) and 3D flight to maximize the total throughput. First, we formulate a Markov decision process (MDP) problem by modeling the mobility of the UAV/vehicles and the state transitions. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient (DDPG), and propose three solutions with different control objectives. Considering the energy consumption of 3D flight, we extend the proposed solutions to maximize the total throughput per energy unit by encouraging or discouraging the UAV's mobility. To achieve this goal, the DDPG framework is modified. Thirdly, in a simplified model with small state space and action space, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Zhu¹,

Liu²,

Wang³

2019

Preprint

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“…Deep neural network (DNN) implements complex non-linear relationship by distributed hierarchical feature representation (Jiao, Wu, Bie, Umek, and Kos, 2018;Shi, Guo, Niu, and Zhan, 2020;Xia, Wang, and Guo, 2020;Chen, Song, Hwang, and Wu, 2020). Many neural networks are proposed to assist traffic prediction, such as artificial neural network, RBF neural network, RNN and long short-term memory neural network (Jiang et al, 2018;Liu, Zhang, and Chen, 2019;Ke, Shi, Guo, and Chen, 2018). Abdi and Moshiri (2015) introduced a new method for short-term traffic flow prediction based on artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction

Pan

Pan³

et al. 2023

Journal of Database Management

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Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

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“…After analyzing the vehicle speed, road, and weather features in the course of the operation of the bus, Wang et al [17] established a prediction model of bus travel time based on LightGBM algorithm. Huang et al [18] constructed deep belief neural network based on multitask learning to predict traffic volume [19]. Zhang et al [2] constructed a short-term traffic flow prediction model based on the fusion algorithm of XGBoost and LightGBM.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm

Wang

Chen

et al. 2021

Journal of Advanced Transportation

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In order to improve the prediction accuracy of train passenger load factor of high-speed railway and meet the demand of different levels of passenger load factor prediction and analysis, the influence factor of the train passenger load factor is analyzed in depth. Taking into account the weather factor, train attribute, and passenger flow time sequence, this paper proposed a forecasting method of train passenger load factor of high-speed railway based on LightGBM algorithm of machine learning. Considering the difference of the influence factor of the passenger load factor of a single train and group trains, a single train passenger load factor prediction model based on the weather factor and passenger flow time sequence and a group of trains’ passenger load factor prediction model based on the weather factor, the train attribute, and passenger flow time sequence factor were constructed, respectively. Taking the train passenger load factor data of high-speed railway in a certain area as an example, the feasibility and effectiveness of the proposed method were verified and compared. It is verified that LightGBM algorithm of machine learning proposed in this paper has higher prediction accuracy than the traditional models, and its scientific and accurate prediction can provide an important reference for the calculation of passenger ticket revenue, operation benefit analysis, etc.

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Review on Deep Learning in Intelligent Transportation Systems

Cited by 5 publications

References 24 publications

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction

Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm

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