3D-CLoST: A CNN-LSTM Approach for Mobility Dynamics Prediction in Smart Cities

Fiorini, Stefano; Pilotti, Giorgio; Ciavotta, Michele; Maurino, Andrea

doi:10.1109/bigdata50022.2020.9378429

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…In this direction, deep learning-based approaches have been recently proposed, which exploit architectures able to capture spatial and temporal patterns. For this reason, the authors in [8,9,25] have combined convolution layers and LSTMs to capture both aspects: compared to models using only convolutions, they try to strengthen the model's ability to identify temporal patterns. However, unlike STREED-Net, these are not autoencoders and do not explore the possibility to employ more advanced solutions beyond LSTMs, like Multiplicative Cascade Units (CMUs).…”

Section: Related Workmentioning

confidence: 99%

“…Then, to take into account the temporal dependence, over the time horizon T (divided into H time points), the flow representation is extended to a tensor of four dimensions F ∈ R H×N×M×C , which represents the main input to our problem. The problem at issue then becomes predicting F t given a volume, that is a sequence of past tensors V ⊂ F. It is worth noting that the resulting problem shows several similarities with the frame prediction problem [8] since the tensor F can be seen as a four dimensional volume composed of H consecutive images, each of which featuring C channels.…”

Section: Problem Statementmentioning

confidence: 99%

“…3D-CLoST [8]: the model uses sequential 3D convolutions to capture spatio-temporal dependencies. Afterwards, a fully-connected layer encloses the information learned in a one-dimensional vector that is finally passed to an LSTM block.…”

Section: Mst3d [7]mentioning

confidence: 99%

“…The literature already proposes several models to address the traffic flow prediction problem; some use convolutions [4,6,7], and others use a combination of convolutions and LSTM [8,9], just to mention the most common approaches. Nevertheless, many architectural solutions have not been fully investigated, and there is often a lack of clear indication as to which features most impact the performance of a model.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, STREED-Net is the first autoencoder architecture that combines the use of time-distributed convolutional blocks with residual connections, a CMUs and two different attention mechanisms. Moreover, unlike other state-of-the-art models [4,[6][7][8], focuses only on recent time dependencies (closeness), that is it only relies on a small number of time periods preceding the one to be predicted. This results in fewer hyperparameters to tuned.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

2022

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In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Mst3d [7]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

2022

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On-Street Parking Prediction: A Comparative Study

Fiorini,

Ciavotta,

Liberto

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

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On the adoption of e-moped sharing systems

et al. 2022

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Recent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study.

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3D-CLoST: A CNN-LSTM Approach for Mobility Dynamics Prediction in Smart Cities

Cited by 4 publications

References 26 publications

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

On-Street Parking Prediction: A Comparative Study

On the adoption of e-moped sharing systems

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