Travel time reliability in transportation networks: A review of methodological developments

TL;DR

This paper reviews the methodological developments in modeling travel time reliability (TTR) in transportation networks, focusing on characterization, evaluation, valuation, and traffic assignment, highlighting their interrelations and future research directions.

Contribution

It provides an integrated framework for understanding TTR modeling from a network perspective, connecting different research areas and addressing uncertainty propagation.

Findings

Comprehensive overview of TTR characterization, evaluation, and valuation methods.

Analysis of TTR effects on individual travel behavior and network flow.

Discussion of future research directions in the context of emerging technologies.

Abstract

The unavoidable travel time variability in transportation networks, resulted from the widespread supply side and demand side uncertainties, makes travel time reliability (TTR) be a common and core interest of all the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling TTR. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for reviewing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically, the TTR characterization provides a whole picture of travel time distribution in transportation networks. TTR evaluation and TTR valuation (known as the value of reliability, VOR) simply and intuitively interpret abstract characterized TTR to be well understood by different stakeholders of transportation systems. TTR-based traffic assignment investigates the effects of TTR on the individual users travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows us to better understand their relationships and may contribute to developing possible combinations of TTR modeling philosophy. Also, the network perspective enables to focus on common challenges of modeling TTR, especially the uncertainty propagation from the uncertainty sources to the TTR at spatial levels including link, route, and the entire network. Some directions for future research are discussed in the era of new data environment, applications, and emerging technologies.