Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Ma, Xiaolei; Luan, Sen; Ding, Chuan; Liu, Haode; Wang, Yunpeng

doi:10.1109/mits.2019.2919504

Cited by 21 publications

(9 citation statements)

References 35 publications

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“…However, it is difficult to directly model the various dependencies of traffic data in the time series. On the contrary, the traffic flow prediction algorithm based on machine learning can effectively deal with the complex non‐linear problems of traffic flow data and has good prediction performance by comprehensively considering the historical regularity of traffic flow data and the spatial correlation of the road network [4]. Therefore, the method based on machine learning has become the mainstream research direction in traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Yao

Wang³

et al. 2023

IET Intelligent Trans Sys

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Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.

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Section: Introductionmentioning

confidence: 99%

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Yao

Wang³

et al. 2023

IET Intelligent Trans Sys

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“…Generally, these studies proposed probabilistic models to retrieve traffic flow features under assumptions on the statistical properties of the traffic data [11,12]. It is common among these studies to assume the existence of spatial autocorrelation among traffic data [13]. However, these interpolation models might be vulnerable under complex traffic patterns and extreme outliers.…”

Section: Introductionmentioning

confidence: 99%

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

Zhong

Kim

Zheng

2023

IEEE Open J. Intell. Transp. Syst.

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While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. These days, other data sources such as vehicle trajectory data collected from vehicle tracking sensors are also incorporated. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole population, where the exact route sampling rate is unknown and may vary over space and time. In this paper, we develop a method that leverage these two limited data sources to enhance link flow estimation. This study proposes a novel generative modelling framework, where we formulate a vehicle's link-to-link movements as a sequential decision-making problem using the Markov Decision Process framework. We propose an Inverse Reinforcement Learning-based method, based on which synthetic population vehicle trajectories can be generated to estimate link flows across the whole network. The proposed method ensures the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data. The proposed generative modelling framework is compared to two existing methods in a synthetic road network and validated in a real road network.

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“…In the last decade, many interpolation approaches have been proposed for missing traffic data imputation. For example, Ma et al proposed a copula-based model for missing traffic data imputation [14]; Li et al proposed an algorithm based on the spatial-temporal queuing mode [15]; Soriguera and Robuste found that the interpolation approaches omitting traffic dynamics and queue evolution cannot achieve accurate traffic data imputation and prediction [16].…”

Section: Introductionmentioning

confidence: 99%

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

Su,

Sun,

Song

et al. 2023

IJGI

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With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.

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Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Cited by 21 publications

References 35 publications

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

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