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Modeling double time-scale travel time processes with application to assessing the resilience of transportation systems
Transportation Research Part B: Methodological ( IF 6.8 ) Pub Date : 2019-05-16 , DOI: 10.1016/j.trb.2019.05.005
R.X. Zhong , X.X. Xie , J.C. Luo , T.L. Pan , W.H.K. Lam , A. Sumalee

This paper proposes a double time-scale model to capture the day-to-day evolution along with the within-day variability of travel time. The proposed model can be used to evaluate short-term to long-term effects of new transport policies and construction of critical infrastructures, and to analyze the resilience of road networks under disruptions. The within-day travel time variability is modeled using the functional data analysis, in which the effects of road traffic congestion and noise of traffic data are considered explicitly. The within-day process is then regarded as the local volatility (or the noise process) to drive the day-to-day process while the latter is described by a modified geometric Brownian motion (GBM). Then, the day-to-day travel time process is obtained by the statistics of the modified GBM. The model probabilistically captures the evolution of day-to-day and within-day travel time processes analytically. Moreover, an efficient method based on the cross-entropy method is developed for calibrating the model parameters. A lasso-like regularization is employed to guarantee a small bias between the model estimations and the measurement counterparts. Finally, two empirical studies are carried out to validate the proposed model at different scales with different traffic scenarios, i.e., a case study on the Guangzhou Airport Expressway (link to path scale) under traffic accident conditions and a case study in New York City (city-scale) to analyze the network resilience under Hurricane Sandy. Both case studies adopted probe vehicle data but with different configurations (fine versus coarse, small versus big data). The empirical studies confirm that the proposed model can accommodate the inherent variability in traffic conditions and data meanwhile being computationally tractable. The numerical results illustrate the applicability of the proposed model as a powerful tool in practice for analyzing the short-term and long-term impacts of disruptions and systematic changes in the performance of road networks.



中文翻译:

对双重时标旅行时间流程进行建模,并用于评估运输系统的弹性

本文提出了一个双时标模型来捕获每日的演变以及行进时间的日内变化。提出的模型可用于评估新交通政策和关键基础设施建设的短期和长期影响,并分析在破坏情况下道路网络的弹性。使用功能数据分析对一天中的旅行时间变异性进行建模,其中明确考虑了道路交通拥堵和交通数据噪声的影响。然后,将日内过程视为局部波动性(或噪声过程),以驱动日常过程,而后者则通过修改的几何布朗运动(GBM)进行描述。然后,通过修改后的GBM的统计信息获得日常旅行时间过程。该模型以概率方式捕获了日常和一天之内旅行时间过程的演变。此外,开发了一种基于交叉熵方法的有效方法来校准模型参数。像套索一样的正则化用于保证模型估计和测量对应项之间的较小偏差。最后,我们进行了两项实证研究,以验证该模型在不同交通场景下的不同规模下的适用性,即以广州机场高速公路交通事故条件下的案例研究(链接到路径规模)和纽约市的案例研究(城市规模)以分析飓风桑迪下的网络弹性。这两个案例研究都采用了探测车辆数据,但具有不同的配置(精细数据与粗略数据,小数据与大数据)。实证研究证实,所提出的模型可以适应交通状况和数据的固有可变性,同时具有可计算性。数值结果说明了所提出的模型在实践中作为强大工具的适用性,可用于分析干扰和道路网络性能的系统性变化的短期和长期影响。

更新日期:2020-02-21
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