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

  • 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.

Author Biography

Vytautas Dumbliauskas, Vilnius Gediminas Technical University
Urban Engineering Department

References

Wang F, Hu L, Zhou D, Sun R, Hu J, Zhao K. Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream. Ad Hoc Networks. 2015;35: 3-13.

Alexander L, Jiang S, Murga, M, González M C. Origin-destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies. 2015;58: 240-250.

Iqbal MS, Choudhury CF, Wang P, González MC. Development of origin-destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies. 2014;40: 63-74.

Toole JL, Colak S, Sturt B, Alexander LP, Evsukoff A, Gonzalez MC. The path most travelled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies. 2015;58: 162-177.

Calabrese F, Di Lorenzo G, Liu L, Ratti C. Estimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area. IEEE Pervasive Computing. 2011;10(4): 36-44.

Widhalm P, Yang Y, Ulm M, Athavale S, Gonzalez MC. Discovering urban activity patterns in cell phone data. Transportation. 2015;42(4): 597-623.

Çolak S, Alexander LP, Alvim BG, Mehndiratta SR, González MC. Analysing Cell Phone Location Data for Urban Travel. Transportation Research Record: Journal of the Transportation Research Board. 2015;2526: 126-135.

Bar-Gera H. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel. Transportation Research Part C: Emerging Technologies. 2007;15(6): 380-391.

Lovell DJ. Accuracy of Speed Measurements from Cellular Phone Vehicle Location Systems. ITS Journal - Intelligent Transportation Systems Journal. 2001;6(4): 303-325.

Dong H, Wu M, Ding X, Chu L, Jia L, Qin Y, Zhou X. Traffic zone division based on big data from mobile phone base stations. Transportation Research Part C: Emerging Technologies. 2015;58: 278-291.

Patire AD, Wright M, Prodhomme B, Bayen AM. How much GPS data do we need? Transportation Research Part C: Emerging Technologies. 2014;58: 325-342.

Lane N, Miluzzo E, Lu H. A Survey of Mobile Phone Sensing. IEEE Communications Magazine. 2010;48(9): 140-150.

Bierlaire M, Chen J, Newman J. A probabilistic map matching method for smartphone GPS data. Transportation Research Part C: Emerging Technologies. 2013;26: 78-98.

Goh C, Dauwels J, Mitrovic N. Online Map-Matching based on Hidden Markov Model for Real-Time Traffic Sensing Applications. The 15th International IEEE Conference on Intelligent Transportation Systems. 16-19 Sep. 2012, Anchorage, AK, USA. IEEE; 2012. p. 776-781.

Reddy S, Mun M, Burke J, Estrin D, Hansen M, Srivastava M. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks. 2010;6(2): 1-27.

Widhalm P, Nitsche P, Brändle N. Transport Mode Detection with Realistic SmartphoneSensor Data. 21st International Conference on Pattern Recognition (ICPR 2012), 11-15 Nov. 2012, Tsukuba, Japan. IEEE; 2012. p. 573-576.

Bohte W, Maat K. Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies. 2009;17(3): 285-297.

Nitsche P, Widhalm P, Breuss S, Maurer P. A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys. Procedia – Social and Behavioral Sciences. 2012;48: 1033-1046.

Chen J, Bierlaire M. Probabilistic Multimodal Map Matching with Rich Smartphone Data. Journal of Intelligent Transportation Systems. 2015;19(2): 134-148.

Wang F, Xu Y. Estimating O-D travel time matrix by Google Maps API: implementation, advantages, and implications. Annals of GIS. 2011;17(4): 199-209.

The Department of Statistics. Official Statistics Portal. 2015. Available from: osp.stat.gov.lt

Barth D. The Bright Side of Sitting in Traffic: Crowdsourcing Road Congestion Data. 2009. Available from: https://googleblog.blogspot.co.uk/2009/08/brightside-of-sitting-in-traffic.html

Seidl DE, Jankowski P, Tsou MH. Privacy and spatial pattern preservation in masked GPS trajectory data. International Journal of Geographical Information Science. 2015;30(4): 1-16.

Gonder J, Burton E, Murakami E. Archiving Data from New Survey Technologies: Enabling Research with High-precision Data While Preserving Participant Privacy. Transportation Research Procedia. 2015;11: 85-97.

Google. Google Maps Distance Matrix API. Developer’s Guide. Available from: https://developers.google.com/maps/documentation/distance-matrix/intro#Introduction

The Python Software Foundation. NumPy: Array Processing for Numbers, Strings, Records, and Objects. Available from: https://pypi.python.org/pypi/numpy

Waskom M. Seaborn: Statistical Data Visualization. 2017. Available from: https://stanford.edu/~mwaskom/software/seaborn/#

QGIS. QGIS User Guide. Available from: http://docs.qgis.org/2.8/en/docs/index.html

Herrera JC, Work DB, Herring R, Ban X, Jacobson Q, Bayen AM. Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research Part C: Emerging Technologies. 2010;18(4): 568-583.

Published
2017-12-21
How to Cite
1.
Dumbliauskas V, Grigonis V, Barauskas A. Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study. PROMET [Internet]. 2017Dec.21 [cited 2020Apr.4];29(6):613-21. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2369
Section
Articles

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