Temporal Disaggregation of Performance Measures to Manage Uncertainty in Transportation Logistics and Scheduling
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 1
Abstract
Transportation planning for highways is informed by performance metrics with aggregated data that can obfuscate the uncertainty of performance conditions across hours, days, and weeks. Recent advances in data collection methods provide disaggregated speed data at a regional level, which can be processed to inform planners and stakeholders of system performance hotspots. Based on the methods of corridor trace analysis (CTA), this paper extended the framework through temporal disaggregation of highway performance metrics, classified as a temporal corridor trace analysis, t-CTA. This approach introduces a temporal weight and temporal value associated with observed performance during discrete periods. The temporal value allows stakeholders to address uncertainty in logistics and scheduling, adjusting the significance of measured performance based on when the adverse conditions are observed. The t-CTA framework provides for analysis of multiple performance criteria across geographic regions and multiple temporal domains. This approach was demonstrated for a limited-access highway and principal arterial road network evaluated across five disparate periods. The availability of probe speed data and accessibility of the t-CTA framework has implications for national planning initiatives that must consider multiple objectives.
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Data Availability Statement
Some data, models, or code generated or used during the study are proprietary or confidential in nature and may be provided only with restrictions. Per the data use agreement (DAU) for the Probe Data Analytics Suite (University of Maryland Center for Advanced Transportation Technology Laboratory n.d.), the probe speed data are subject to restrictions on transferring or disclosing the data to entities that are not licensed to receive such data without prior written authorization from the data vendors (INRIX). Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including portions of the code and processed data used in the demonstration.
Acknowledgments
This effort has been supported in part by the Virginia Department of Transportation (VDOT), the Virginia Transportation Research Council (VTRC) and the Commonwealth Center for Advanced Logistics Systems (CCALS).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 1 • March 2021
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© 2020 American Society of Civil Engineers.
History
Received: Feb 7, 2020
Accepted: Jul 10, 2020
Published online: Oct 19, 2020
Published in print: Mar 1, 2021
Discussion open until: Mar 19, 2021
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