Prediction of Taxi Demand Based on ConvLSTM Neural Network

Li, Pengcheng; Sun, Min; Pang, Mingzhou

doi:10.1007/978-3-030-04221-9_2

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The most representative one is ConvLSTM [27], which fuses convolutional operations into an LSTM neural network to model the spatial and temporal dependence of precipitation in satellite maps. Li et al [28] applied ConvLSTM to the traffic domain by visualizing urban traffic flows as pictures for modelling and segmenting the road map of a city as a large grid. The pixel values of each grid represented the traffic flows in the region, and Con-vLSTM was used to model the spatial and temporal dependence of traffic flows between neighbouring grids.…”

Section: Traffic Predictionmentioning

confidence: 99%

“…Li et al. [28] applied ConvLSTM to the traffic domain by visualizing urban traffic flows as pictures for modelling and segmenting the road map of a city as a large grid. The pixel values of each grid represented the traffic flows in the region, and ConvLSTM was used to model the spatial and temporal dependence of traffic flows between neighbouring grids.…”

Section: Related Workmentioning

confidence: 99%

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MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction

Zhao,

Chao,

Zhang

et al. 2023

IET Intelligent Trans Sys

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With the development of intelligent transportation technology, road travel time prediction has become an important research direction. Owing to the complex periodic dependence and non‐linear features of road travel time series, achieving accurate and effective predictions remains a challenging task. Most existing traffic sequence prediction methods lack modelling of the dynamic correlation between multiple period information, resulting in unsatisfactory prediction results. To address this, a multi‐component attention graph convolutional network (MCAGCN) is proposed for road travel time prediction. First, the spatial‐temporal features of three historical components (hourly, daily and weekly) are modelled individually. A skip attention layer is then used to fuse multi‐scale spatial‐temporal features to enhance the model's feature extraction capabilities. Secondly, a component attention layer is proposed to calculate the correlation between different components using the temporal features of the prediction moment, to achieve dynamic modelling between different period information. The experimental results on the Tianchi, METR‐LA, and PeMS‐BAY datasets, which are real‐world traffic forecasting datasets, demonstrate the superiority of MCAGCN.

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Section: Traffic Predictionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction

Zhao,

Chao,

Zhang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Huang et al [36] designed a ConvLSTM-Inception network (CL-IncNet) to make spatiotemporal predictions of traffic flow data. Li et al [37] constructed a ConvLSTM network to predict taxi demand, which was shown to more accurately process spatial information. Chen et al [38] proposed a BT-ConvLSTM model to introduce temporal information to a ConvLSTM network, and it was experimentally shown to improve traffic flow prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network

et al. 2022

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Online car-hailing has brought convenience to daily travel, whose accurate prediction benefits drivers and helps managers to grasp the characteristics of urban travel, so as to facilitate decisions. Spatiotemporal prediction in the transportation field has usually been based on a recurrent neural network (RNN), which has problems such as lengthy computation and backpropagation. This paper describes a model based on a Transformer, which has shown success in computer vision. The study area is divided into grids, and the structure of travel data is converted into video frames by time period, based on predicted spatiotemporal travel demand. The predictions of the model are closest to the real data in terms of spatial distribution and travel demand when the data are divided into 10 min intervals, and the travel demand in the first two hours is used to predict demand in the next hour. We experimentally compare the proposed model with the three most commonly used spatiotemporal prediction models, and the results show that our model has the best accuracy and training speed.

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“…e results show that the prediction accuracy is improved compared with LSTM and its variants. To improve the accuracy of taxi demand prediction, Li et al [28] proposed a model based on deep learning, which uses a ConvLSTM network to capture spatiotemporal features.…”

Section: Traffic Flow Predictionmentioning

confidence: 99%

A Sparse Gating Convolutional Recurrent Network for Traffic Flow Prediction

Huang

Tang

Peng

et al. 2022

Mathematical Problems in Engineering

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With the continuous development of deep learning, more and more huge deep learning models are developed by researchers, which leads to an exponential increase of the parameters of models. Therein, the convolutional recurrent network as a type of widely used deep learning method is often employed to handle spatiotemporal data, e.g., traffic data. However, because of the large number of parameters in the model, the convolutional recurrent network needs to consume a lot of computing resources and time in the training process. To reduce the consumption of resources, we propose a sparse convolutional recurrent network with a sparse gating mechanism that is able to reduce the complexity of the network by an improved gate unit while keeping the performance of the model. We evaluate the performance of our proposed network on traffic flow datasets, and the experimental results show that the parameters of the model are significantly reduced under the condition of similar prediction accuracy compared with the traditional convolutional recurrent network.

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Prediction of Taxi Demand Based on ConvLSTM Neural Network

Cited by 7 publications

References 8 publications

MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction

MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction

Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network

A Sparse Gating Convolutional Recurrent Network for Traffic Flow Prediction

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