DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level

By Xuan Song, Hiroshi Kanasugi, Ryosuke Shibasaki. 2016

233 citations recorded by the source corpus.

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[116] developed a system based on DNN models to achieve this goal on a city level.”
High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “Multiple novel LSTM based models, such as bidirectional LSTM [14], deep LSTM [15], shared hidden LSTM [38], and nested LSTM [39], have been designed via reorganizing and combing single LSTM models and applied to capture comprehensive temporal dependencies for traffic prediction.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “On the other hand, Song et al. [234] constructed a deep RNN to predict urban human mobility.”
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “Multiple novel LSTM based models, such as bidirectional LSTM [13], deep LSTM [14], shared hidden LSTM [37], and nested LSTM [38], have been designed via reorganizing and combing single LSTM models and applied to capture comprehensive temporal dependencies for traffic prediction.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “shed light on the mobility analysis at a larger scale [260].”
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, unknown): “Several studies [2, 22-25, 34, 43, 44, 45] have been done to examine the applicability of LSTMs in traffic forecasting, and the results demonstrate the advantages of LSTMs.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “[335] proposed an intelligent deep LSTM RNN based system for predicting both human mobility and the specific transportation mode in a large-scale transportation network, which was beneficial in terms of providing accurate traffic control for intelligent transportation systems (ITS).”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “hen employed the auto-encoder framework to learn fixed length deep representations , Vol. 1, No. 1, Article . Publication date: March 2020. 26 Wang, et al. of trajectories for clustering. Song et al. [150] proposed a model named DeepTransport to predict the transportation mode such as walk, taking t”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “In such a case, RNN and LSTM models can be applied [37], [57], [70], [80], [126], [154], [156].”
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction (2018, methodology): “Recently, LSTM-based methods have been used in citywide level applications like public transportation prediction [21] and location prediction problem [17].”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, background): “] (2) 3.2. Long Short-Term Memory It has been showed that LSTMs work well on sequence-based tasks with long-term dependencies Duan et al. (2016);Chen et al. (2016);Zhao et al. (2017);Wu and Tan(2016);Song et al. (2016);Yu et al. (2017a). Although a variety of LSTM variants were proposed in recent ye”
GMAN: A Graph Multi-Attention Network for Traffic Prediction (2019, background): “…networks (RNN) are the trafﬁc condition of sensor 3 at time step t + l + 1 may be more correlated to that of distant time steps (e.g., t − 1 ) rather than recent time steps (e.g., t + l ). widely applied to model such temporal correlations (Ma et al. 2015; Song, Kanasugi, and Shibasaki 2016).”
When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks (2018, background): “(Song, Kanasugi, and Shibasaki 2016) built an intelligent system called DeepTransport, for simulating the human mobility and transportation mode at a citywide level.”
Deep Distributed Fusion Network for Air Quality Prediction (2018, background): “proposed a recurrent neural network to simulate and predict human mobility [25].”
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “proposed a recurrent-neural-network-based model to predict the person’s future movement [51].”