Predicting travel time reliability using mobile phone GPS data

2017 ◽  
Vol 75 ◽  
pp. 30-44 ◽  
Author(s):  
Dawn Woodard ◽  
Galina Nogin ◽  
Paul Koch ◽  
David Racz ◽  
Moises Goldszmidt ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability

scholarly journals A methodology for forecasting freeway travel time reliability using GPS data

2017 ◽  
Vol 25 ◽  
pp. 842-852 ◽  
Author(s):  
Zun Wang ◽  
Anne Goodchild ◽  
Edward McCormack
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability

Measuring Truck Travel Time Reliability Using Truck Probe GPS Data

2015 ◽  
Vol 20 (2) ◽  
pp. 103-112 ◽  
Author(s):  
Zun Wang ◽  
Anne Goodchild ◽  
Edward McCormack
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability

Travel Time Reliability Analysis on Selected Bus Route of Mysore Using GPS Data

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Akhilesh Chepuri ◽  
Chetan Kumar ◽  
Pooja Bhanegaonkar ◽  
Shriniwas S. Arkatkar ◽  
Gaurang Joshi
Keyword(s):  
Reliability Analysis ◽  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability

Measurement of travel time reliability of road transportation using GPS data: A freight fluidity approach

2019 ◽  
Vol 130 ◽  
pp. 240-288 ◽  
Author(s):  
Miguel Gastón Cedillo-Campos ◽  
Carlos Mario Pérez-González ◽  
Jared Piña-Barcena ◽  
Eric Moreno-Quintero
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability ◽  
Road Transportation

Urban Travel Time Reliability Analysis with Consumer GPS Data

2015 ◽  
Vol 2497 (1) ◽  
pp. 73-83
Author(s):  
Qianfei Li ◽  
Jingtao Ma ◽  
Mehrnaz Ghamami ◽  
Yu (Marco) Nie
Keyword(s):  
Reliability Analysis ◽  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability ◽  
Urban Travel

scholarly journals Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model

2020 ◽  
Vol 13 (1) ◽  
pp. 429-445
Author(s):  
Xiaoxu Chen ◽  
Xiangdong Xu ◽  
Chao Yang
Keyword(s):  
Computational Complexity ◽  
Mobile Phone ◽  
Travel Time ◽  
Mobile Devices ◽  
Mixed Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Gps Data ◽  
Inference Method ◽  
Acceptable Accuracy

Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.

Travel Time Reliability

2021 ◽  
pp. 109-121
Author(s):  
Sharmili Banik ◽  
Anil Kumar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Travel Time Reliability
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