Search

Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets. [ABSTRACT FROM AUTHOR]

The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomous driving technology. For the purpose of risk identification, the actual operation data for autonomous buses in Shanghai were converted into 3 kinds of grayscale images and 1 kind of radar image from a temporal–spatial perspective, and a deep learning convolutional neural network, AlexNet, was applied for image recognition. This article uses several image data augmentation strategies to address the problem of uneven distribution of samples and compares the effectiveness of different strategies. The optimal accuracy (ACC) of the risk identification was 90.4%, the optimal true positive rate (TPR) was 83.7%, and the optimal false negative rate (FPR) was 94.58%. The accuracy of risk identification using AlexNet was higher based on the sample containing Fourier images. In addition, risk identification accuracy based on grayscale images was higher than that based on radar images. Autonomous buses were found to be vulnerable to risks in areas such as turning sections and intersections. In addition, the results show that the Fourier transform, an image augmentation strategy, can effectively solve the uneven sample distribution problem, and the length of the input time series has an impact on the accuracy of the risk identification. [ABSTRACT FROM AUTHOR]

The role of the stock market in the whole financial market is indispensable. How to obtain the actual trading income and maximize the interests in the trading process has been a problem studied by scholars and financial practitioners for a long time. Deep learning network can extract features from a large number of original data, which has potential advantages for stock market prediction. Based on the Shanghai and Shenzhen stock markets from 2019 to 2021, we use LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We select the right hyperparameters at the beginning of our tests, use RBM pre-processing data, then use LSTM model to obtain expected stock return, to effectively predict future stock market analysis and predictive behavior. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model. [ABSTRACT FROM AUTHOR]

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning, a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically, a trajectory encoding module is designed to mine the semantic information of trajectory data, and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular, a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai, and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists. [ABSTRACT FROM AUTHOR]