SVM‐based hybrid approach for corridor‐level travel‐time estimation

Sharmila, R.; Velaga, Nagendra R.; Kumar, Akhilesh

doi:10.1049/iet-its.2018.5069

Cited by 24 publications

(12 citation statements)

References 85 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to note that in addition to the travel time-related temporal features, spatial features such as segment ID and the segment length are also included. Road geometric characteristics, such as segment (intersection) length and location information, are all potentially influential factors for modeling TTP [22]. In short, both spatial and temporal characteristics of travel time can significantly improve the TTP accuracy by reducing the time-lag problems between the experienced and predicted travel times on travel routes [23].…”

Section: Travel Time Datamentioning

confidence: 99%

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Fan

2021

Sustainability

View full text Add to dashboard Cite

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

show abstract

Section: Travel Time Datamentioning

confidence: 99%

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Fan

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Most of the recent predictions of travel time have adopted machine learning methods [13,14] including k-nearest neighbor (KNN) algorithms [15,16], support vector machines [17], and neural networks [18,19] model. Compared to earlier statistical prediction methods, machine learning models do not assume any specific model structure for the data, but treat it as unknown, which can handle complex problems and large amounts of data well.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection and Model Fusion Approach for Predicting Urban Macro Travel Time

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction.

show abstract

“…In this paper, we select the SVM model as the base learner and use the RS algorithm to combine SVM classifiers. The SVM model is a kernel learning method that applies the training set to construct a hyperplane for classifying test samples [42][43][44]. In this paper, we study the freeway traffic state identification problem as the multiclassifier, and therefore, must construct appropriate multiple classifiers.…”

Section: ) Base Learner Of Svm Modelmentioning

confidence: 99%

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

et al. 2020

View full text Add to dashboard Cite

SVM‐based hybrid approach for corridor‐level travel‐time estimation

Cited by 24 publications

References 85 publications

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Feature Selection and Model Fusion Approach for Predicting Urban Macro Travel Time

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Contact Info

Product

Resources

About