Modeling bicycle volume using crowdsourced data from Strava smartphone application
|نوع نگارش مقاله||
scopus – master journals – JCR
۴٫۲۷۶ در سال ۲۰۲۰
۲۶ در سال ۲۰۲۱
۰٫۹۰۱ در سال ۲۰۲۰
|شاخص Quartile (چارک)||
Q1 در سال ۲۰۲۰
خرید محصول توسط کلیه کارت های شتاب امکان پذیر است و بلافاصله پس از خرید، لینک دانلود محصول در اختیار شما قرار خواهد گرفت و هر گونه فروش در سایت های دیگر قابل پیگیری خواهد بود.
فهرست مطالب مقاله:
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:|
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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