Urban link travel time estimation based on sparse probe vehicle data

2013 ◽  
Vol 31 ◽  
pp. 145-157 ◽  
Author(s):  
Fangfang Zheng ◽  
Henk Van Zuylen
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

scholarly journals Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Xiyang Zhou ◽  
Zhaosheng Yang ◽  
Wei Zhang ◽  
Xiujuan Tian ◽  
Qichun Bing
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Time Window ◽  
Low Frequency ◽  
Time Estimation ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on theK-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.

scholarly journals Travel Time Estimation by Combining Probe-Vehicle Data and VICS Data

2006 ◽  
Vol 23 ◽  
pp. 1011-1018 ◽  
Author(s):  
Lixiao WANG ◽  
Mei Ian JIANG ◽  
Toshiyuki Yamamoto ◽  
Taka MORIKAWA
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data

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

Reconstructing Vehicle Trajectories to Support Travel Time Estimation

2018 ◽  
Vol 2672 (42) ◽  
pp. 148-158 ◽  
Author(s):  
Zheng Li ◽  
Robert Kluger ◽  
Xianbiao Hu ◽  
Yao-Jan Wu ◽  
Xiaoyu Zhu
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Input Data ◽  
Time Estimation ◽  
Smartphone Application ◽  
Primary Objective ◽  
Travel Time Estimation ◽  
K Nearest Neighbors ◽  
Probe Vehicle

The primary objective of this study was to increase the sample size of public probe vehicle-based arterial travel time estimation. The complete methodology of increasing sample size using incomplete trajectory was built based on a k-Nearest Neighbors ( k-NN) regression algorithm. The virtual travel time of an incomplete trajectory was represented by similar complete trajectories. As incomplete trajectories were not used to calculate travel time in previous studies, the sample size of travel time estimation can be increased without collecting extra data. A case study was conducted on a major arterial in the city of Tucson, Arizona, including 13 links. In the case study, probe vehicle data were collected from a smartphone application used for navigation and guidance. The case study showed that the method could significantly increase link travel time samples, but there were still limitations. In addition, sensitivity analysis was conducted using leave-one-out cross-validation to verify the performance of the k-NN model under different parameters and input data. The data analysis showed that the algorithm performed differently under different parameters and input data. Our study suggested optimal parameters should be selected using a historical dataset before real-world application.

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