Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89103
PIRA download icon_1.1View/Download Full Text
Title: Modeling double time-scale travel time processes with application to assessing the resilience of tranportotion systems
Authors: Zhong, RX 
Xie, XX
Luo, JC
Pan, TL
Lam, WHK 
Sumalee, A 
Issue Date: 2018
Source: Transportation research procedia, 2018, v. 38, p. 524-543
Abstract: 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 variabile 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 me 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 travey time process is obtained by the statistic 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 ths 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 differed scales with different traffic scenarios, i.e., a case study on thy 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 ueder 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 applicabiiity 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.
Keywords: Day-To-Day
Double time-Scale dynamics
Dynamic travel time distribution
Traffic network resilience
Within-Day
Publisher: Elsevier
Journal: Transportation research procedia 
EISSN: 2352-1465
DOI: 10.1016/j.trpro.2019.05.028
Description: 23rd International Symposium on Transportation and Traffic Theory, ISTTT 2019, 24-26 July 2018
Rights: © 2019 The Authors.
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) (https://creativecommons.org/licenses/by-nc-nd/4.0/)
The following publication Zhong, R. X., Xie, X. X., Luo, J. C., Pan, T. L., Lam, W. H. K., & Sumalee, A. (2018). Modeling double time-scale travel time processes with application to assessing the resilience of tranportotion systems. Paper presented at the Transportation Research Procedia, , 38 524-543 is available at https://dx.doi.org/10.1016/j.trpro.2019.05.028
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
1-s2.0-S2352146519300377-main.pdf3.22 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

20
Citations as of May 22, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.