scholarly journals On the Short-term Prediction of Traffic State: An Application on Urban Freeways in ROME

2015 ◽  
Vol 10 ◽  
pp. 176-185 ◽  
Author(s):  
L. Mannini ◽  
S. Carrese ◽  
E. Cipriani ◽  
U. Crisalli
Keyword(s):  
Short Term ◽  
Traffic State ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Arash Rasaizadi ◽  
Seyedehsan Seyedabrishami ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Road Traffic ◽  
Support Vector ◽  
Paper Machine ◽  
Short Term ◽  
Traffic State ◽  
Rural Road ◽  
Term Prediction ◽  
Short Term Prediction

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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