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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
21.12.2017
DOI
https://doi.org/10.7307/ptt.v29i6.2369
ISSUE
2017 (Vol 29), Issue 6
LICENSE
Copyright (c) 2024 Vytautas Dumbliauskas, Vytautas Grigonis, Andrius Barauskas
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Authors:

Vytautas Dumbliauskas
Vilnius Gediminas Technical University

Vytautas Grigonis
Vilnius Gediminas Technical University

Andrius Barauskas
Vilnius Gediminas Technical University

Keywords:Kaunas City, Google Traffic Data, Python, Skim Matrix,

Abstract

Recently, new traffic data sources have emerged raising new challenges and opportunities when applying novel methodologies. The purpose of this research is to analyse car travel time data collected from smartphones by Google Company. Geographic information system (GIS) tools and Python programming language were employed in this study to establish the initial framework as well as to automatically extract, analyse, and visualize data. The analysis resulted in the calculation of travel time fluctuation during the day, calculation of travel time variability and estimation of origin-destination (OD) skim matrices. Furthermore, we accomplished the accessibility analysis and provided recommendations for further research.

References

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How to Cite
Dumbliauskas, V. (et al.) 2017. Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study. Traffic&Transportation Journal. 29, 6 (Dec. 2017), 613-621. DOI: https://doi.org/10.7307/ptt.v29i6.2369.

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