An Efficient Method for Accelerating Training of Short-Term Traffic Prediction Models in Large-Scale Traffic Networks

2020 ◽  
Author(s):  
Athanasios I. Salamanis ◽  
George A. Gravvanis ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Tzovaras
Keyword(s):  
Efficient Method ◽  
Large Scale ◽  
Prediction Models ◽  
Traffic Prediction ◽  
Traffic Networks ◽  
Short Term

Short-Term Traffic Prediction Based on DeepCluster in Large-Scale Road Networks

2019 ◽  
Vol 68 (12) ◽  
pp. 12301-12313 ◽  
Author(s):  
Lingyi Han ◽  
Kan Zheng ◽  
Long Zhao ◽  
Xianbin Wang ◽  
Xuemin Shen
Keyword(s):  
Large Scale ◽  
Road Networks ◽  
Traffic Prediction ◽  
Short Term

Short-Term Metro Ridership Prediction During Unplanned Events

2021 ◽  
pp. 036119812110375
Author(s):  
Yangyang Zhao ◽  
Zhenliang Ma ◽  
Xinguo Jiang ◽  
Haris N. Koutsopoulos
Keyword(s):  
Prediction Accuracy ◽  
Large Scale ◽  
Prediction Models ◽  
Imbalanced Data ◽  
Short Term ◽  
Training Problem ◽  
Term Prediction ◽  
Model Training ◽  
Short Term Prediction ◽  
The Impact

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

scholarly journals Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhijin Wang ◽  
Yaohui Huang ◽  
Bingyan He ◽  
Ting Luo ◽  
Yongming Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
High Morbidity ◽  
Infectious Diarrhea ◽  
Short Term ◽  
Search Data ◽  
Parsimonious Model ◽  
Using Data ◽  
Short Term Prediction ◽  
And Control

Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.

scholarly journals Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rusul L. Abduljabbar ◽  
Hussein Dia ◽  
Pei-Wei Tsai
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Traffic Prediction ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Bidirectional Lstm ◽  
Using Data

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

scholarly journals Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models

2005 ◽  
Vol 29 (6) ◽  
pp. 475-489 ◽  
Author(s):  
Henrik Madsen ◽  
Pierre Pinson ◽  
George Kariniotakis ◽  
Henrik Aa. Nielsen ◽  
Torben S. Nielsen
Keyword(s):  
Wind Power ◽  
Electricity Markets ◽  
Large Scale ◽  
Prediction Models ◽  
Wind Farms ◽  
Offshore Wind ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Prediction Systems

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 226-226
Keyword(s):  
Prostate Cancer ◽  
Knowledge Base ◽  
Prediction Models ◽  
Precision Oncology ◽  
Castration Resistant Prostate Cancer ◽  
Short Term ◽  
Castration Resistant

Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017 Jul;2017 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28890946/ Newton Y, Novak AM, Swatloski T, McColl DC, Chopra S, Graim K, Weinstein AS, Baertsch R, Salama SR, Ellrott K, Chopra M, Goldstein TC, Haussler D, Morozova O, Stuart JM. TumorMap: exploring the molecular similarities of cancer samples in an interactive portal. Cancer Res 2017 Nov 1;77(21):e111-e114 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29092953/ Seyednasrollah F, Koestler DC, Wang T, Piccolo SR, Vega R, Greiner R, Fuchs C, Gofer E, Kumar L, Wolfinger RD, Winner KK, Bare C, Neto EC, Yu T, Shen L, Abdallah K, Norman T, Stolovitzky G, Soule HR, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Elo LL, Zhou FL, Guinney J, Costello JC, and Prostate Cancer DREAM Challenge Community. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clin Cancer Inform 2017 Aug 4;(1):1-15 http://ascopubs.org/doi/abs/10.1200/CCI.17.00018

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