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Bicycle Traffic Volume Estimation Based on GPS Data

Pogodzinska, Sylwia ; Kiec, Mariusz and D'Agostino, Carmelo LU (2020) 2019 Transport Infrastructure and Systems in a Changing World. Towards a more Sustainable, Reliable and Smarter Mobility, TIS Roma 2019 In Transportation Research Procedia 45. p.874-881
Abstract

All the analytic methods for assessing the safety or comfort of bicyclists in urban area have as a common factor the number of bicycles that enter the system in a certain time interval or an estimate of that. The estimation of the average bicycle volume based on manual and automatic measurements is time-consuming and often require the use of expensive technology. The paper presents a method of estimation based on GPS data from a bike sharing system as a low-cost option for data collection. The analysis was made for the city of Krakow (Poland), using the daily volume of bicycles from 5 automatic counter loops and GPS data from a bike sharing system called Wavelo. Based on the two-factor analysis of variance (ANOVA) and the Tukey post-hoc... (More)

All the analytic methods for assessing the safety or comfort of bicyclists in urban area have as a common factor the number of bicycles that enter the system in a certain time interval or an estimate of that. The estimation of the average bicycle volume based on manual and automatic measurements is time-consuming and often require the use of expensive technology. The paper presents a method of estimation based on GPS data from a bike sharing system as a low-cost option for data collection. The analysis was made for the city of Krakow (Poland), using the daily volume of bicycles from 5 automatic counter loops and GPS data from a bike sharing system called Wavelo. Based on the two-factor analysis of variance (ANOVA) and the Tukey post-hoc test, the influence of "localization" and "day of the week" factors on the share of Wavelo bicycles in the entire bicycle flow was estimated. It was shown that examined share is not significantly different between individual days of the week, but changes significantly between analyzed locations. Developed models are characterized by high R2 coefficients (exceeding 0.90) and the average error of estimation up to 11.5%. The results of the studies show that bicycle volume can be estimated based on GPS data from bike sharing system. However, it is necessary to carry out control measurements to verify developed models and their possible application in other locations.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
bicycle, bike sharing system, GPS, volume
in
Transportation Research Procedia
volume
45
pages
8 pages
publisher
Elsevier
conference name
2019 Transport Infrastructure and Systems in a Changing World. Towards a more Sustainable, Reliable and Smarter Mobility, TIS Roma 2019
conference location
Rome, Italy
conference dates
2019-09-23 - 2019-09-24
external identifiers
  • scopus:85083439957
ISSN
2352-1457
DOI
10.1016/j.trpro.2020.02.081
language
English
LU publication?
yes
id
54db3b38-bd35-4d85-b1b8-a92fb9fd8ede
date added to LUP
2020-05-07 15:22:33
date last changed
2020-12-29 03:50:29
@article{54db3b38-bd35-4d85-b1b8-a92fb9fd8ede,
  abstract     = {<p>All the analytic methods for assessing the safety or comfort of bicyclists in urban area have as a common factor the number of bicycles that enter the system in a certain time interval or an estimate of that. The estimation of the average bicycle volume based on manual and automatic measurements is time-consuming and often require the use of expensive technology. The paper presents a method of estimation based on GPS data from a bike sharing system as a low-cost option for data collection. The analysis was made for the city of Krakow (Poland), using the daily volume of bicycles from 5 automatic counter loops and GPS data from a bike sharing system called Wavelo. Based on the two-factor analysis of variance (ANOVA) and the Tukey post-hoc test, the influence of "localization" and "day of the week" factors on the share of Wavelo bicycles in the entire bicycle flow was estimated. It was shown that examined share is not significantly different between individual days of the week, but changes significantly between analyzed locations. Developed models are characterized by high R<sup>2</sup> coefficients (exceeding 0.90) and the average error of estimation up to 11.5%. The results of the studies show that bicycle volume can be estimated based on GPS data from bike sharing system. However, it is necessary to carry out control measurements to verify developed models and their possible application in other locations.</p>},
  author       = {Pogodzinska, Sylwia and Kiec, Mariusz and D'Agostino, Carmelo},
  issn         = {2352-1457},
  language     = {eng},
  month        = {03},
  pages        = {874--881},
  publisher    = {Elsevier},
  series       = {Transportation Research Procedia},
  title        = {Bicycle Traffic Volume Estimation Based on GPS Data},
  url          = {http://dx.doi.org/10.1016/j.trpro.2020.02.081},
  doi          = {10.1016/j.trpro.2020.02.081},
  volume       = {45},
  year         = {2020},
}