As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any specific prediction models. First, we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape. Using the noisy predictions, we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance. Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors. In particular, the optimized performance can improve under non-Gaussian vs. Gaussian noise. We also find that dynamic routing could reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 EUR/year in terms of Value of Travel Time Savings for the case study.

翻译：随着公共交通(PT)变得更加活跃和需求响应,它越来越依赖于对运输需求的预测。但是,这种预测对PT的有效运行的准确需要程度如何?我们通过在丹麦首都哥本哈根进行的对PT旅行的试验性案例研究来解决这个问题,我们独立于任何具体的预测模型进行这种研究。首先,我们模拟需求预测中的错误,通过无偏见的噪音分布,其形状差异很大。我们利用噪音预测,然后通过线性编程设计来模拟和优化对需求的PT车队,并衡量其性能。我们的结果表明,最佳性能主要受到噪音分布的扭曲和不经常出现的大预测错误的影响。特别是,优化性能可以在非Gausian和Gaussian的噪音下得到改善。我们还发现动态路线安排可以将出行时间减少至少23%,而静态路由。从案例研究的旅行节省时间价值来看,估计每年减少809,000欧元。