Dataset Information

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

ABSTRACT: In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

SUBMITTER: Zhang C

PROVIDER: S-EPMC6795449 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

Zhang Chen C He Jie J Liu Ziyang Z Xing Lu L Wang Yinhai Y

PloS one 20191016 10

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network ...[more]

PMID: 31618244

Similar Datasets

Project description:Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. The software package, source code, and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM.

Dataset Information

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

Publications

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets