Evaluating the Potential Use of Crowdsourced Bicycle Data in North Carolina
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2019-09-01
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Corporate Contributors:University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
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Edition:Final Report
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Abstract:Cycling, as a healthier and greener travel mode, has been encouraged for short-distance trips by city planners and policymakers. Since cycling provides an efficient way to improve public health, alleviate traffic congestion, and reduce energy consumption, it is essential to analyze the contributing factors to the cyclist’s route choice on each roadway segment, so as to quantify the impact of certain attributes on bicycle volume and further provide a better cycling environment for cyclists to encourage non-motorized travel. To map ridership, data including network characteristics, sociodemographic factors, time of day, and day of week, are quite indispensable. There have been multiple data collection methods and the most commonly used ones include traditional manual counts, travel surveys, and crowdsourced data from the third party. Most of the previous research efforts used the first two methods to collect the data of interest. However, such methods are expensive and time-consuming. Crowdsourced data, on the contrary, are cost effective and time-saving, and therefore they have been widely collected and used by many public agencies and private sectors. Among all the crowdsourced data, data collected from smartphone applications including Strava, CycleTracks, ORcycle, etc. have become more and more prevalent. Crowdsourcing has increased the availability of data collection and provided an efficient way to bridge the data gap for decision making and performance measures. This research focuses on evaluating the potential use of crowdsourced bike data and comparing them with the traditional bike counting data that are collected in the city of Charlotte. Using the bike data both from the Strava smartphone cycling application and from the bicycle count stations, the bicycle volume models are developed. Based on the results, a predictive model is concluded, and a map illustrating the bicycle volume on most of the road segments in the city of Charlotte is generated. In addition, to gain a better understanding of the attributes that have an impact on cycling, other supporting data are also collected and combined with the Strava bicycle count data. An ordered probit model is developed to analyze the Strava users’ cycling route segment choice. Finally, recommendations are made in order to help improve the cycling environment and increase the bicycle volume in the future.
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