A genetic algorithm-based method for improving quality of travel time prediction intervals

2011 ◽  
Vol 19 (6) ◽  
pp. 1364-1376 ◽  
Author(s):  
Abbas Khosravi ◽  
Ehsan Mazloumi ◽  
Saeid Nahavandi ◽  
Doug Creighton ◽  
J.W.C. Van Lint
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Prediction Intervals ◽  
Travel Time Prediction ◽  
Time Prediction

Prediction Intervals to Account for Uncertainties in Travel Time Prediction

2011 ◽  
Vol 12 (2) ◽  
pp. 537-547 ◽  
Author(s):  
Abbas Khosravi ◽  
Ehsan Mazloumi ◽  
Saeid Nahavandi ◽  
Doug Creighton ◽  
J. W. C. van Lint
Keyword(s):  
Travel Time ◽  
Prediction Intervals ◽  
Travel Time Prediction ◽  
Time Prediction

scholarly journals Daily Commute Time Prediction Based on Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Fang Zong ◽  
Haiyun Lin ◽  
Bo Yu ◽  
Xiang Pan
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Time Allocation ◽  
Choice Model ◽  
Probit Model ◽  
Hazard Model ◽  
Continuous Model ◽  
Management Policy ◽  
Travel Time Prediction ◽  
Time Prediction

This paper presents a joint discrete-continuous model for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA) is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity-travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity-travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete-continuous model can be used to predict the agenda of a simple daily activity-travel pattern containing only work, and it provides potential for transportation demand management policy analysis.

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.

Sign in / Sign up

Export Citation Format

Share Document