To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

为了有效管理和控制公共交通运营，了解影响公交车到达延误的各种因素至关重要。然而，有限的研究集中在对公交延误因素的综合分析上，往往依赖于单步延误预测模型，无法解释公交线路沿线时空因素的异质影响。为了分析公交车到站延误因素的异质影响，本文提出了一组以公交车位置为条件的回归方程。开发了一个看似不相关的回归方程 (SURE) 模型来估计回归系数，解释由方程之间共享的未观察因素引起的回归残差之间的潜在相关性。该模型使用瑞典斯德哥尔摩的公交运营数据进行了验证。结果强调了开发特定站点的公交车到达延误模型以了解解释变量的异质影响的重要性。影响公交车到达延误的重要因素主要与公交车运营有关，例如连续上游站点的延误、停留时间、预定行程时间、反复拥堵和当前交通状况。日历和天气等因素对抵达延误有显着但边际的影响。研究建议，应根据站点路段的特点，制定不同的公交运营管理策略，如时刻调整、线路优化、实时监控等，因为这些因素的影响因站点位置而异。