AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation

Xu, Meng; Di, Yining; Ding, Hongxing; Zhu, Zheng; Chen, Xiqun; Yang, Hai

doi:10.1016/j.commtr.2023.100099

Cited by 16 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, when dealing with MNAR scenarios, more intricate methods are necessary for imputation and prediction, and most approaches for handling MNAR data are based on neural network methods [7]. This includes techniques that combine generative adversarial networks (GANs) [21,22], LSTM [21], CNNs [23], and AGNPs [24] and utilize probabilistic models to estimate the distribution of missing values [25,26]. Moreover, there exists a region-based approach for handling urban similarity matching [27].…”

Section: Prediction and Imputation Algorithmsmentioning

confidence: 99%

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Li,

Liu,

Chen

2023

Sustainability

View full text Add to dashboard Cite

Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems and urban planning. Nonetheless, missing data in traffic datasets is a common challenge due to equipment failures, communication issues, and monitoring limitations, especially the missing not at random (MNAR) problem. This research introduces an approach to address MNAR-type missing data in traffic status prediction, utilizing a multidimensional feature sequence and a second-order hidden Markov model (2nd-order HMM). First, this approach involves extracting spatiotemporal features for the preset data sections and spatial features for the sections to be predicted based on the traffic spatiotemporal characteristics. Second, using the extracted features, distinctive road traffic features are generated for each section. Furthermore, at specific intervals within the defined time period, nearest distance feature matching is introduced to ascertain the traffic attributes of the road section under prediction. Finally, relying on the matched status results, a 2nd-order HMM is employed to forecast the traffic status for subsequent moments within the defined time period. Experiments were carried out using datasets from Shenzhen City and compared against the hidden Markov models and contrast measure (HMM-C) method to affirm the efficacy of the proposed approach.

show abstract

Section: Prediction and Imputation Algorithmsmentioning

confidence: 99%

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Li,

Liu,

Chen

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The core of explicit communication is to explore how intelligent agents communicate. Among them, the selection of communication objects can be achieved through heuristic frameworks [25,26] and gating mechanisms [27]; the adjustment of the communication content and time is based on DL methods, such as attention mechanisms [18,28], recurrent neural networks [29], and graph neural networks [30][31][32]. Implicit communication mainly affects the behavioral strategy formulation of the signal agent through value function decomposition and centralized value function [20,23,[33][34][35].…”

Section: Introductionmentioning

confidence: 99%

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

Chang,

Ji,

Wang

et al. 2024

Sustainability

View full text Add to dashboard Cite

Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections.

show abstract

“…However, obtaining the true distributions of uncertain parameters is often challenging. Abundant historical data on uncertain parameters and their related features present new perspectives for addressing uncertainty [2], in fields like transportation [3,4] and logistics [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation

Tian,

Jiang,

Pang

et al. 2024

Mathematics

View full text Add to dashboard Cite

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems.

show abstract

AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation

Cited by 16 publications

References 53 publications

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation

Contact Info

Product

Resources

About