Running Time Prediction for Signalized Urban Streets

2011 ◽  
Vol 2257 (1) ◽  
pp. 22-30 ◽  
Author(s):  
James A. Bonneson ◽  
Michael P. Pratt ◽  
Mark A. Vandehey
Keyword(s):  
Running Time ◽  
Urban Streets ◽  
Time Prediction

Learning Running-time Prediction Models for Gene-Expression Analysis Workflows

2015 ◽  
Vol 13 (9) ◽  
pp. 3088-3095
Author(s):  
David A. Monge ◽  
Matej Holec ◽  
Filip Zelezny ◽  
Carlos Garcia Garino
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Prediction Models ◽  
Running Time ◽  
Time Prediction

scholarly journals RHMX: Bus arrival time prediction via mixed model

2021 ◽  
Vol 5 (6) ◽  
pp. 30-43
Author(s):  
Fei Jia ◽  
Huibing Zhang ◽  
Xiaoli Hu
Keyword(s):  
Mixed Model ◽  
Arrival Time ◽  
Time Series Data ◽  
Stopping Time ◽  
Intelligent Transportation ◽  
Series Data ◽  
Running Time ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

With the widespread use of information technologies such as IoT and big data in the transportation business, traditional passenger transportation has begun to transition and upgrade into intelligent transportation, providing passengers with a better riding experience. Giving precise bus arrival times is a critical link in achieving urban intelligent transportation. As a result, a mixed model-based bus arrival time prediction model (RHMX) was suggested in this work, which could dynamically forecast bus arrival time based on the input data. First, two sub-models were created: bus station stopping time prediction and interstation running time prediction. The former predicted the stopping time of a running bus at each downstream station in an iterative manner, while the latter projected its running time on each downstream road segment (stations as the break points). Using the two models, a group of time series data on interstation running time and bus station stopping time may be predicted. Following that, the time series data from the two sub-models was fused using long short-term memory (LSTM) to generate an approximate bus arrival time. Finally, using Kalman filtering, the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time. The experimental findings showed that the suggested model's accuracy and stability were both improved by 35% and 17%, respectively, over AutoNavi and Baidu.

A hybrid model to improve the train running time prediction ability during high-speed railway disruptions

2020 ◽  
Vol 122 ◽  
pp. 104510 ◽  
Author(s):  
Ping Huang ◽  
Chao Wen ◽  
Liping Fu ◽  
Qiyuan Peng ◽  
Zhongcan Li
Keyword(s):  
Hybrid Model ◽  
High Speed ◽  
Prediction Ability ◽  
High Speed Railway ◽  
Running Time ◽  
Time Prediction

scholarly journals A DFT-Based Running Time Prediction Algorithm for Web Queries

2021 ◽  
Vol 13 (8) ◽  
pp. 204
Author(s):  
Oscar Rojas ◽  
Veronica Gil-Costa ◽  
Mauricio Marin
Keyword(s):  
High Performance ◽  
Web Search ◽  
Back Propagation ◽  
Prediction Algorithm ◽  
Feed Forward Neural Network ◽  
Running Time ◽  
Practical Applications ◽  
Time Prediction ◽  
Speed Up ◽  
Index Service

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.

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