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Xiaolei Ma
Researcher at Beihang University
Publications - 83
Citations - 8640
Xiaolei Ma is an academic researcher from Beihang University. The author has contributed to research in topics: Deep learning & Vehicle routing problem. The author has an hindex of 29, co-authored 83 publications receiving 5977 citations. Previous affiliations of Xiaolei Ma include Chinese Ministry of Public Security & University of Washington.
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Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
TL;DR: A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.
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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
TL;DR: Wang et al. as mentioned in this paper proposed a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy.
Posted Content
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
TL;DR: The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks and outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time.
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Vehicle Routing Problem: Simultaneous Deliveries and Pickups with Split Loads and Time Windows
TL;DR: In this article, the authors formulated the vehicle routing problem of simultaneous deliveries and pickups with split loads and time windows (VRPSDPSLTW) as a mixed-integer programming problem and developed a hybrid heuristic algorithm to solve this problem.
Journal ArticleDOI
Mining smart card data for transit riders’ travel patterns
TL;DR: Wang et al. as mentioned in this paper proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China and identified trip chains based on the temporal and spatial characteristics of their smart card transaction data.