In this paper we use large-scale mass transit data to improve the journey time performance measurement of urban bus systems. For low frequency bus services, the application of on-time performance (OTP) metrics, particularly the location and number of stops to sample, varies greatly across operators, which can lead to biased estimates. In this paper, we aim to address sampling disparity, and propose a new statistically robust sampling scheme to ensure representative, non-biased, and comparable measurement of route level on time performance (OTP) which enables the inclusion of operators with sparse data in benchmarking activities. We use automated vehicle location data and analyse 59 unique low frequency route-direction data sets from 9 international bus operators. Across all route-direction data sets, an average of 28% of stops are required to be sampled under the defined sampling scheme to achieve a 95% confidence level of sampling the route-level mean within a $\pm$ 5% range of accuracy. By including data on additional routes, the analysis can be further refined and generalised to enable progressive improvement of inter-operator comparability and benchmarking.

在本文中，我们使用大规模公共交通数据来改进城市公交系统的行程时间性能测量。对于低频公交服务，准点率 (OTP) 指标的应用，特别是采样的位置和站点数量，在不同运营商之间差异很大，这可能导致估计有偏差。在本文中，我们旨在解决采样差异问题，并提出一种新的统计鲁棒采样方案，以确保对路线准时性能（OTP）水平进行代表性、无偏见和可比较的测量，从而能够将数据稀疏的运营商纳入基准测试中活动。我们使用自动车辆定位数据并分析来自 9 个国际巴士运营商的 59 个独特的低频路线方向数据集。在所有路线方向数据集中，需要在定义的采样方案下对平均 28% 的停靠点进行采样，才能在某个范围内对路线级别平均值进行采样的 95% 置信度。$\pm$5% 的准确度范围。通过纳入其他路线的数据，可以进一步细化和概括分析，以逐步改进运营商之间的可比性和基准测试。