Urban Arterial Travel Time Prediction Using Support Vector Regression

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Anna Mary Philip ◽  
Gitakrishnan Ramadurai ◽  
Lelitha Vanajakshi
Keyword(s):  
Support Vector Regression ◽  
Travel Time ◽  
Support Vector ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Urban Arterial

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

scholarly journals Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

Logistics ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Nikolaos Servos ◽  
Xiaodi Liu ◽  
Michael Teucke ◽  
Michael Freitag
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tracking Data ◽  
Travel Time Prediction ◽  
Adaptive Boosting ◽  
Time Prediction ◽  
The Usa

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches.

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