论文标题
关于动态公共交通需求预测的质量要求
On the Quality Requirements of Demand Prediction for Dynamic Public Transport
论文作者
论文摘要
随着公共交通（PT）变得更加动态和需求响应，它越来越取决于交通需求的预测。但是，这种预测对于有效的PT操作的准确性如何？我们通过对丹麦大都会哥本哈根PT旅行的实验案例研究来解决这个问题，我们独立于任何特定的预测模型进行。首先，我们通过无偏见的噪声分布来模拟需求预测的错误。然后，使用嘈杂的预测，我们通过线性编程公式模拟和优化需求响应的PT机队并衡量其性能。我们的结果表明，优化的性能主要受噪声分布的偏差和很少大的预测误差的影响。特别是，在非高斯与高斯噪声下，优化的性能可以改善。我们还发现，动态路由可以将行程时间减少至少23％与静态路由相比。根据案例研究的旅行时间节省价值，这种减少估计为809,000欧元/年。
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.
