scholarly journals Determining the Required Probe Vehicle Size for Real-Time Travel Time Estimation on Signalized Arterial

IEEE Access ◽  
2018 ◽  
pp. 1-1 ◽  
Author(s):  
Lili Lu ◽  
Xuan Li ◽  
Pengjun Zheng ◽  
Kaihao Wang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Size

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

2020 ◽  
Vol 4 (1) ◽  
pp. 1-23
Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation

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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