Abstract Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches. With the emergence of smartphones and navigation apps, location-based and crowdsourced Big Data are receiving increased attention. In this regard, location-based big data (LocBigData) collected from probe vehicles and road users can be used to provide speed and travel time information in different locations. Examining the quality of crowdsourced data is essential for researchers and agencies before using them. This study assessed the quality of Waze speed data from surface streets and conducted a case study in Sevierville, Tennessee. Typically, examining the quality of these data in surface streets and arterials is more challenging than freeways data. This research used Bluetooth speed data as the ground truth, which is independent of Waze data. In this study, three steps of methodology were used. In the first step, Waze speed data was compared to Bluetooth data in terms of accuracy, mean difference, and distribution similarity. In the second step, a k-means algorithm was used to categorize Waze data quality, and a multinomial logistics regression model was performed to explore the significant factors that impact data quality. Finally, in the third step, machine learning techniques were conducted to predict the data quality in different conditions. The result of the comparison showed a similar pattern and a slight difference between datasets, which verified the quality of Waze speed data. The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours. Also, the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets. Finally, the result of machine learning prediction showed that a KNN method performed the highest prediction accuracy of 84.5% and 82.9% of the time for training and test datasets, respectively. Overall, the study results suggest that Waze speed data is a promising data source for surface streets.

中文翻译：

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地面街道上基于位置的众包速度数据的质量：田纳西州塞维尔维尔 Waze 和蓝牙速度数据的案例研究

摘要 获取准确的速度和行程时间信息是研究人员、地理学家和交通机构面临的挑战。过去，交通数据通常由政府机构通过固定位置的传感器获取和传播。这些传感器设备的高成本、基础设施需求和低覆盖水平要求机构和研究人员超越传统方法。随着智能手机和导航应用程序的出现，基于位置和众包的大数据越来越受到关注。在这方面，从探测车辆和道路使用者收集的基于位置的大数据（LocBigData）可用于提供不同位置的速度和旅行时间信息。在使用众包数据之前，检查众包数据的质量对于研究人员和机构至关重要。该研究评估了来自地面街道的 Waze 速度数据的质量，并在田纳西州塞维尔维尔进行了案例研究。通常，检查地面街道和主干道中这些数据的质量比高速公路数据更具挑战性。该研究使用蓝牙速度数据作为基本事实，独立于 Waze 数据。在这项研究中，使用了三个步骤的方法论。第一步，将 Waze 速度数据与蓝牙数据在准确性、均值差异和分布相似性方面进行比较。第二步，使用k-means算法对Waze数据质量进行分类，并通过多项逻辑回归模型探索影响数据质量的重要因素。最后，在第三步中，使用机器学习技术来预测不同条件下的数据质量。比较的结果显示出相似的模式和数据集之间的细微差异，这验证了位智速度数据的质量。统计模型表明，Waze 速度数据在高峰时段比夜间更准确。此外，交通速度、交通量和路段长度与地面街道上 Waze 数据的准确性有显着关联。最后，机器学习预测的结果表明，KNN 方法对训练和测试数据集的预测准确率最高，分别为 84.5% 和 82.9%。总体而言，研究结果表明 Waze 速度数据是一种很有前途的地面街道数据源。统计模型表明，Waze 速度数据在高峰时段比夜间更准确。此外，交通速度、交通量和路段长度与地面街道上 Waze 数据的准确性有显着关联。最后，机器学习预测的结果表明，KNN 方法对训练和测试数据集的预测准确率最高，分别为 84.5% 和 82.9%。总体而言，研究结果表明 Waze 速度数据是一种很有前途的地面街道数据源。统计模型表明，Waze 速度数据在高峰时段比夜间更准确。此外，交通速度、交通量和路段长度与地面街道上 Waze 数据的准确性有显着关联。最后，机器学习预测的结果表明，KNN 方法对训练和测试数据集的预测准确率最高，分别为 84.5% 和 82.9%。总体而言，研究结果表明 Waze 速度数据是一种很有前途的地面街道数据源。机器学习预测结果表明，KNN 方法对训练和测试数据集的预测准确率最高，分别为 84.5% 和 82.9%。总体而言，研究结果表明 Waze 速度数据是一种很有前途的地面街道数据源。机器学习预测结果表明，KNN 方法对训练和测试数据集的预测准确率最高，分别为 84.5% 和 82.9%。总体而言，研究结果表明 Waze 速度数据是一种很有前景的地面街道数据源。