Convolutional LSTM based transportation mode learning from raw GPS trajectories

Nawaz, Asif; Huang, Zhiqiu; Wang, Senzhang; Hussain, Yasir; Khan, Izhar Ahmed; Khan, Zaheer

doi:10.1049/iet-its.2019.0017

Cited by 42 publications

(34 citation statements)

References 44 publications

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“…Unlike the matrix multiplication in LSTM, ConvLSTM uses the Convolutional operation in both states to state and input to state transitions. ConvLSTM also evaluates the future state of the cell using current input and past states of its neighbors [ 23 ]. Mathematically, ConvLSTM can be represented as in Eqs.…”

Section: Methodsmentioning

confidence: 99%

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

et al. 2021

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The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-021-00408-3.

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Section: Methodsmentioning

confidence: 99%

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

et al. 2021

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“…In particular, they design an end-to-end classification framework based on the bidirectional LSTM (long short-term memory), which is one kind of RNN architecture. Also, Qin and Nawaz [112,113] apply the LSTM model to recognize or learn transportation modes. Differently, before using the LSTM model to capture the temporal dependencies characteristics on the feature vectors, they first uses a CNN model to learn appropriate and robust feature representations for transportation modes recognition.…”

Section: Traffic Classificationmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

215

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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“…The spatio-temporal features of the trajectory images are used in the prediction while the influence of space and weather are ignored [20]. GPS data is used to discover the impact of weather on human flow patterns [21]. The spatio-temporal semantics of the area are inferred to improve the accuracy [22].…”

Section: Related Workmentioning

confidence: 99%

A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

You

Guan

et al. 2021

IJGI

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Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance between the taxis’ supplies and demands. Considering the life schedule of urban residents and the different functions of geogrid regions, the research developed in this paper introduces a spatio-temporal schedule-based neural network for urban taxi waiting time prediction. The approach integrates a series of multi-source data from taxi trajectories to city points of interest, different time frames and human behaviors in the city. We apply a grid-based and functional structuration of an urban space that provides a lower-level data representation. Overall, the neural network model can dynamically predict the waiting time of taxi passengers in real time under some given spatio-temporal constraints. The experimental results show that the granular-based grids and spatio-temporal neural network can effectively predict and optimize the accuracy of taxi waiting times. This work provides a decision support for intelligent travel predictions of taxi waiting time in a smart city.

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Convolutional LSTM based transportation mode learning from raw GPS trajectories

Cited by 42 publications

References 44 publications

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

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