Modeling double time-scale travel time processes with application to assessing the resilience of transportation systems

•Data-driven and model-driven approaches are integrated to develop a double time-scale travel time model.•The model probabilistically captures the day-to-dau evolution along with within day variability of travel time.•Direct input of raw traffic data with dynamic and noisy nature reflecting the effe...

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Bibliographic Details
Published in:Transportation research. Part B: methodological 2020-02, Vol.132, p.228-248
Main Authors: Zhong, R.X., Xie, X.X., Luo, J.C., Pan, T.L., Lam, W.H.K., Sumalee, A.
Format: Article
Language:English
Subjects:
Accident conditions
Airports
Brownian motion
Case studies
Data analysis
Day-to-day
Double time-scale dynamics
Driving conditions
Dynamic travel time distribution
Empirical analysis
Entropy
Evolution
Hurricanes
Long-term effects
Noise
Regularization
Resilience
Roads
Scale models
Statistical analysis
Traffic accidents
Traffic congestion
Traffic information
Traffic network resilience
Transportation networks
Transportation systems
Travel
Travel time
Variability
Volatility
Within-day
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Summary:•Data-driven and model-driven approaches are integrated to develop a double time-scale travel time model.•The model probabilistically captures the day-to-dau evolution along with within day variability of travel time.•Direct input of raw traffic data with dynamic and noisy nature reflecting the effects of traffic congestion and external uncertainties is enabled in the day-to-day time-scale.•The day-to-day evolution from the transient impact to the steady-state behavior is captured. 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 chan
ISSN:0191-2615
1879-2367
DOI:10.1016/j.trb.2019.05.005