Modeling bicycle volume using crowdsourced data from Strava smartphone application

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جزئیات بیشتر

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۲۰۲۱

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scopus – master journals – JCR

ایمپکت فاکتور

۴٫۲۷۶ در سال ۲۰۲۰

شاخص H_index

۲۶ در سال ۲۰۲۱

شاخص SJR

۰٫۹۰۱ در سال ۲۰۲۰

شاخص Quartile (چارک)

Q1 در سال ۲۰۲۰

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رفرنس

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توضیحات مختصر محصول
Modeling bicycle volume using crowdsourced data from Strava smartphone application

فهرست مطالب مقاله:

Abstract

Cycling as a healthier and greener travel mode has been more and more popular among cit- izens especially for  short-distance  trips. Since cycling has become an  efficient way to reduce energy consumption, eliminate traffic emissions, and improve public health, it is critical to estimate bicycle volume on  each roadway segment if possible, and to explore its  potential impact on  cycling. Therefore, this paper utilizes a prevalent crowdsourcing- based data collection method to model the bicycle volume in  the City  of Charlotte, North Carolina. The  data are  aggregated by Strava Metro from the users’ smartphone application. To process the data, essential information regarding manual count bicycle volume, crowd- sourced bicycle data, road characteristics data, sociodemographic data, zoning data, tem- poral data, and bicycle facility data are  combined using both the ArcGIS  and SAS. After the data processing step, two linear regression models are  developed to quantify the rela- tionship between bicycle manual count data and Strava Metro bicycle data as well as other relevant variables. Modeling results are  analyzed and bicycle volume on  most of the road segments in  the City  of Charlotte is estimated. A map illustrating the bicycle ridership in the City  of Charlotte is also created.

بخشی از متن مقاله:
  1. Introduction

As an efficient way  of alleviating traffic congestion and improving air quality, cycling has  been encouraged for short dis- tance trips to  provide a healthier and greener travel. To estimate bicycle volume on  each roadway segment and motivate cycling, research needs to  be  conducted to  study the contributing factors to  the bicycle volume and to  investigate the cor- relation between manual count bicycle data and crowdsourced bicycle data. One of the most critical issues for the conduct of such research studies is that the traditional data collection methods have some limitations and their data collecting process can  be  time-consuming and expensive (Musakwa and Selala,  ۲۰۱۶; Boss et al., 2018).

Recently, crowdsourcing has  become prevalent in  transportation planning and management. It offers shared platforms and systems to  invite a  large number of  interested people to  address common issues that affect them all  (Misra et al.,

2014). As crowdsourcing techniques have been developed rapidly, some studies regarding its  use  in  transportation have shown its  immense potential in augmenting the traditional data collection methods. 

Previous research that used crowdsourced data to estimate bicycle volume can be summarized as follows. Griffin and Jiao (2016) selected five  specific monitoring locations with recorded bicycle counts in  downtown  Austin, Texas.  The  data col- lected by CycleTracks smartphone applications, and crowdsourced data derived from the Strava fitness application, together with the traffic counts, were compiled and compared in Geographic Information systems (GIS) at these five locations. This new crowdsourced method was  found to  improve efficiency and provide a dataset that covers a larger area. Jestico et al. (2016) utilized the GIS and developed a generalized linear model to  identify the relationship between crowdsourced data from Strava fitness application and manual counting data in Victoria, British Columbia, Canada. A generalized linear model was  developed to predict categories of bicycle volumes and to create maps. The result showed that in mid-size North Amer- ican  cities within urban areas, the routes recorded in crowdsourced fitness application tend to be similar to those of the com- muter cyclists’. Hochmair et al. (2019) used Strava activity tracking data collected in the Miami-Dade County area to identify which sociodemographic factors, network measures (in  particular on-road bicycle facilities), and place-specific characteris- tics  might influence bicycle ridership. For this purpose, a set  of linear regression models were developed to predict non- commuter and commuter bicycle kilometers traveled, as  well  as  bicycle kilometers traveled on  weekends and weekdays. Eigenvector spatial filtering was  applied to model spatial autocorrelation and to improve accuracy. It was  found that Strava tracking data can  be utilized as a useful dataset to examine the influence of various attributes on bicycle volume, especially for the cycling volume in a larger network. Proulx and Pozdnukhov (2017) developed a novel geographically weighted data fusion-based method to estimate bicycle traffic volumes in a network by fusing crowdsourced data from Strava with usage data from Bay Area  Bikeshare. This  data fusion method has  information combining various types of bicycle travel and can provide a more systematically view of the cycling conditions.

In addition, crowdsourcing has  been used in different research areas (Whitla, 2009; Boulos et al., 2011; Brabham, 2012; Overeem et al., 2013; Martí et al., 2012). Various smartphone applications have been applied to  help collect information about the users’ cycling trips including Strava, CycleTracks, and MapMyRide, etc.  (Figliozzi and Blanc,  ۲۰۱۵), which can  be useful for researchers and planners to  collect bicyclists’ trip trajectories.

Since  crowdsourcing has  many advantages in data collection, it is leveraged in this research study. Based  on the crowd- sourced bicycle data that were collected from the Strava smartphone application, this research is conducted to estimate the bicycle volume on most of the road segments in the City of Charlotte and analyze the relationship between Strava counts and manual bicycle counts in the City of Charlotte.

Although the primary objective of  this research is  to  systematically develop bicycle volume models and analyze the relationship between manual count data and crowdsourced data, it  is  not  accurate enough to  estimate  bicycle volume based on  crowdsourced data only.  Factors that were examined in  the past by  other researchers could affect the volume and have significant impacts on  cycling, including infrastructure  types or  bicycle facilities (e.g.  bicycle lanes or  paths) (Dill and Gliebe, 2008; Charlton et al., 2011; Hood  et al., 2011; Broach et al., 2012; Grond, 2016; Khatri et al., 2016), bicy- clist   demographic data, trip characteristics (e.g.  slope) (Charlton et al.,  ۲۰۱۱; Hood   et al.,  ۲۰۱۱; Broach et al.,  ۲۰۱۲; Zimmermann et al.,  ۲۰۱۷), distance (Broach et al.,  ۲۰۱۲; Zimmermann et al.,  ۲۰۱۷; Casello and Usyukov, 2014; Yeboah and  Alvanides, 2015), roadway  characteristics  (Moore, 2015), time  (Yeboah and  Alvanides, 2015), and  land  use (LaMondia and Watkins, 2017). Therefore, based on  the available information and the data collected, this paper combines all  relevant data that may have potential impacts on  bicycle volume including North Carolina (NC) road characteristics data, demographic data, slope data, manual count data from count stations in  the City  of  Charlotte, temporal data, and bicycle facility data, as  well  as  the crowdsourced bicycle data from Strava Metro. Data  comparison between Strava data and the manual count bicycle data is conducted to demonstrate the differences between these two datasets. Data  process- ing  and combination procedures are  completed using both the ArcGIS and SAS. Based  on  the combined data, two linear regression models are  developed. The  relationship between manual count data and Strava data as  well  as  other relevant data is analyzed. Bicycle  volume on  most of the road segments in  the City  of Charlotte is predicted using the developed model. A bicycle ridership map is also  created to  present a graphical view of the bicycle counts. This  research presents a practical and yet  effective method to estimate the bicycle volume that is very  general and can  be applied to predict bicycle volume at any  other location and/or in  similar cases.

 

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