详情

Integrating Crowdsourced Data with Traditionally Collected Data to Enhance Estimation of Bicycle Exposure Measure

项目名称：	Integrating Crowdsourced Data with Traditionally Collected Data to Enhance Estimation of Bicycle Exposure Measure
摘要：	Although many transportation agencies have invested substantially in efforts to improve cycling environments, limitations on methodologies used to estimate bicycle exposure (i.e., volume) continue to impact the decision process. Traditional methods for measuring bicycle volume have been proven to be challenging and costly, especially when planning for non-motorized facilities at network level. One of the potential supplement to traditional methods is to use crowdsourced cycling data. This study explored the potential of incorporating crowdsourced data in estimation methods to improve the spatial-temporal estimation of bicycle exposure. Different probabilistic and machine learning models were tested, including the Negative Binomial (NB) model, Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbors (KNN). In terms of prediction, the Random Forest model was found to have a better prediction capability. The addition of Strava counts, which had an average observed penetration rate of 7 percent, improved the RF model significantly by increasing its ability to explain variations in hourly bicycle volume from 65 percent (R-Sqrd = 0.65) to 71 percent (R-Sqrd = 0.71). The study also conducted a simulation study to assess the change in model performance based on different simulated Strava penetration rates and found that a unit change in the percent of simulated Strava penetration rate has a very significant influence on the model�s prediction performance. The products of this study can assist planners to make informed decisions currently or in the future by providing them with a reliable ethod for estimating bicycle exposure.
状态：	Completed
资金：	87233
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	Transportation Research Center for Livable Communities
项目负责人：	Dunn, Denise E
执行机构：	Western Michigan University
主要研究人员：	Oh, Jun-Seok
开始时间：	20170815
预计完成日期：	20190630
实际结束时间：	20190613