Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Jiang, Weiwei; Luo, Jiayun

doi:10.48550/arxiv.2103.11824

Cited by 2 publications

(2 citation statements)

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“…Deep learning's achievements in computer vision [29] and natural language processing have made it widely used in traffic prediction [14], [15]. In early works, traffic data was directly used to train a Recurrent Neural Network (RNN) for learning long or short-term dependencies [19].…”

Section: Realated Workmentioning

confidence: 99%

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Gao¹,

Wang²,

Liu³

2021

Preprint

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Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.

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Section: Realated Workmentioning

confidence: 99%

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Gao¹,

Wang²,

Liu³

2021

Preprint

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“…With the development of Information and Communication Technologies (ICTs), there are different approaches of sensing and collecting various crowd flow data, 1 for example, traffic loop detectors, 2 automated fare collection systems for subways and buses, 3 taxi Global Positioning System (GPS) trajectories, 4 etc. Compared with other approaches with fixed stations, for example, subways and buses, taxis can serve the passengers in almost every corner of the city, thus presenting a more comprehensive coverage of the crowd flow patterns.…”

Section: Introductionmentioning

confidence: 99%

TaxiBJ21: An open crowd flow dataset based on Beijing taxi GPS trajectories

Jiang

2021

Internet Technology Letters

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Crowd flow prediction has been a significant problem in smart cities. Real-world datasets are the basis of designing data-driven models, for example, machine learning and deep learning models. However, some well-known datasets, for example, TaxiBJ, become unavailable when the original sharing links are deleted as the data providers move to new jobs. As an alternative choice, TaxiBJ21 is proposed in this letter for future crowd flow prediction studies. This new open dataset contains the taxi inflow and outflow matrices in Beijing for 3 months, with the same data format as TaxiBJ and can be used in the current literature seamlessly. In this letter, the process of constructing TaxiBJ21 is introduced and three baselines including two simple historical models and a multilayer perceptron neural network are evaluated on this new dataset as benchmarks. Both the data and baselines are publicly available in a Github repository.

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Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Cited by 2 publications

References 50 publications

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

TaxiBJ21: An open crowd flow dataset based on Beijing taxi GPS trajectories

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