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Use of LSTM for Short-Term and Long-Term Travel Time Prediction

Irem Islek, Sule Gündüz Ögüdücü
2018 International Conference on Information and Knowledge Management  
In this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York city.  ...  Results of this study show that, LSTM provides satisfying results for long term travel time prediction as well as short term.  ...  Prediction Of Travel Time In this study, we aim to predict travel time for short term and long term using a real world travel time data.  ... 
dblp:conf/cikm/IslekO18 fatcat:j4zhenuoyvgjpedxlcqj3rnxsm

Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model

Xiantong Li, Hua Wang, Pengcheng Sun, Hongquan Zu
2021 Sustainability  
In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal  ...  correlation of travel time on different routes to accurately predict route travel time.  ...  It contains modules such as CNN, temporal modules, attention layer, long-term LSTM (long short-term memory), and short-term LSTM.  ... 
doi:10.3390/su13031253 fatcat:knzrvkriq5d6tjtre75ysiqn7u

An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Xiangdong Ran, Zhiguang Shan, Yufei Fang, Chuang Lin
2019 Sensors  
The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence  ...  In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure.  ...  Acknowledgments: We are thankful to Jicheng Zhou for the help provided with laboratory equipment. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19040861 fatcat:4t76gwvj4vb65h72ozzqeqoeim

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

Chen Zhang, Jie He, Ziyang Liu, Lu Xing, Yinhai Wang, Jinjun Tang
2019 PLoS ONE  
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.  ...  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.  ...  These time steps would be used to address the short-term prediction problems.  ... 
doi:10.1371/journal.pone.0223973 pmid:31618244 pmcid:PMC6795449 fatcat:n6zixhihira7zhhovv64ceh4mm

Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction [article]

Won Kyung Lee, Deuk Sin Kwon, So Young Sohn
2021 arXiv   pre-print
In this study, we propose a dynamically localised long short-term memory (LSTM) model that involves both spatial and temporal dependence between roads.  ...  Many big-data-based prediction approaches have been developed but they do not reflect complicated dynamic interactions between roads considering time and location.  ...  This LSTM network for traffic prediction covers both long-term and short-term changes caused by sudden traffic accidents or other events.  ... 
arXiv:2112.02409v1 fatcat:psz6z2fytvg4boytgv6ofhck2i

Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix [article]

Ayobami E. Adewale, Amnir Hadachi
2020 arXiv   pre-print
In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination  ...  The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.  ...  For example, Duan et al in [7] adopted the use of Long Short-Term Memory (LSTM) architecture to solve travel time prediction problem.  ... 
arXiv:2004.04030v1 fatcat:tuigk77mnnhlbg6lvtxv3fzroi

Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow

Quanchao Chen, Di Wen, Xuqiang Li, Dingjun Chen, Hongxia Lv, Jie Zhang, Peng Gao, Feng Chen
2019 PLoS ONE  
For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow.  ...  LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series.  ...  Acknowledgments The authors thank the National Railway Train Diagram Research and Training Center of Southwest Jiaotong University for providing the data for this study.  ... 
doi:10.1371/journal.pone.0222365 pmid:31509599 pmcid:PMC6738919 fatcat:ngjtxlr67zamxkv6peod36oq6u

A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction

Wei Chen, Zongping Li, Can Liu, Yi Ai, Andrea Monteriù
2021 Journal of Advanced Transportation  
Then, we use a convolutional long short-term memory (Conv-LSTM) network to extract spatial and temporal characteristics to solve the short-term prediction problem of the subway congestion delay in the  ...  delays to the overall travel time of passengers.  ...  ., for providing the necessary data. is research was supported by the National Key Research and Development Program of China (2017YFB1200702).  ... 
doi:10.1155/2021/6645214 fatcat:ykctddfwjzclhmionvgwmb6tim

Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

Wenjun Du, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, Tuo Sun, Qi-zhou Hu
2021 Journal of Advanced Transportation  
This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention  ...  Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction.  ...  of Transportation of Zhejiang Province (2020006) and the National Key Research and Development Program of China (2018YFB1601000).  ... 
doi:10.1155/2021/9512501 fatcat:m7ligmzhpvbntfnewwuitxfamm

Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model

Zhongwei Hou, Zixue Du, Guang Yang, Zhen Yang
2022 Applied Sciences  
To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and  ...  the long short-term memory network (LSTM) based on machine learning is proposed.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12157597 fatcat:7lpwtb46mvcwlapczjhkg3tshe

Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework [article]

Wei Wang Shenzhen Urban Transport Planning Center Co. Ltd, China)
2018 arXiv   pre-print
network (CNN) and long short-term memory (LSTM) models.  ...  To capture traffic seasonal variations, time of the day and day of the week indicators are fused with trained features.  ...  and long short-term memory (LSTM) model technique.  ... 
arXiv:1809.01887v2 fatcat:bz2cgzp5dfcfpoenhyjasqevvu

Analysis and comparison of long short-term memory networks short-term traffic prediction performance

Erdem DOGAN
2020 Scientific Journal of Silesian University of Technology. Series Transport  
Analysis and comparison of long short-term memory networks short-term traffic prediction performance. Summary.  ...  Therefore, the effect of LSTM training set size on performance and optimum training set size for short-term traffic flow prediction problems were investigated in this study.  ...  Long short-term memory Long short-term memory network (LSTM) is an advanced type of recurrent neural networks (RNNs) that can overcome the long-term dependence problem.  ... 
doi:10.20858/sjsutst.2020.107.2 fatcat:4y75ul7kbre7pdyxzono62un54

Forecasting the carsharing service demand using uni and multivariable models

Victor Aquiles Alencar, Lucas Ribeiro Pessamilio, Felipe Rooke, Heder Soares Bernardino, Alex Borges Vieira
2021 Journal of Internet Services and Applications  
More in deep, we evaluate the use of the Long Short-Term Memory (LSTM) and Prophet techniques to predict the demand of three real carsharing services.  ...  When considering the free-floating carsharing service, and prediction for the short-term (i.e., 12 hours), the boosting algorithms (e.g.  ...  Availability of data and materials Please contact author for data requests.  ... 
doi:10.1186/s13174-021-00137-8 fatcat:oznlvfprqzbxjpoesbpqblqgke

Travel Time Prediction on Long-Distance Road Segments in Thailand

Rathachai Chawuthai, Nachaphat Ainthong, Surasee Intarawart, Niracha Boonyanaet, Agachai Sumalee
2022 Applied Sciences  
We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning  ...  This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12115681 fatcat:dyiggkcgw5hyxnx6vuncmcpcuu

Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition

Yi Cao, Xiaolei Hou, Nan Chen
2022 Sustainability  
In order to accurately predict the passenger flow of urban metros in different periods and provide a scientific basis for schedule planning, a short-term metro passenger-flow prediction model is constructed  ...  by integrating ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM) to solve the problem that the existing empirical mode decomposition (EMD) is prone to modal  ...  At the same time, we appreciate the assistance of three graduate students in data processing. We are also grateful to the editors and anonymous reviewers for their suggestions and comments.  ... 
doi:10.3390/su14148562 fatcat:oz3pmb6qlfh5da24btq3cv4rou
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