Link travel time estimation based on connected vehicle probe data

2013 ◽  
Author(s):  
Chen Chen
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Connected Vehicle ◽  
Link Travel Time

Accuracy of Link Travel Time Estimation Using Probe Vehicles

2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Link Travel Time

Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction

2009 ◽  
Vol 36 (4) ◽  
pp. 580-591 ◽  
Author(s):  
Dongjoo Park ◽  
Soyoung You ◽  
Jeonghyun Rho ◽  
Hanseon Cho ◽  
Kangdae Lee
Keyword(s):  
Travel Time ◽  
Mean Square Error ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Mean Square ◽  
Loop Detector ◽  
Link Travel Time ◽  
Travel Time Forecasting

With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.

scholarly journals Urban link travel time estimation using traffic states-based data fusion

2018 ◽  
Vol 12 (7) ◽  
pp. 651-663 ◽  
Author(s):  
Lin Zhu ◽  
Fangce Guo ◽  
John W. Polak ◽  
Rajesh Krishnan
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Link Travel Time

Arterial Link Travel Time Estimation Using Loop Detector Data

1997 ◽  
Author(s):  
Tong Qiang Wu ◽  
Eil Kwon ◽  
Kevin Sommers ◽  
Michael Zhang ◽  
Ahsan Habib
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Loop Detector ◽  
Link Travel Time
Sign in / Sign up

Export Citation Format

Share Document