Li_Travel time reliability and risk.pdf (1.06 MB)
Measuring travel time reliability and risk: A nonparametric approach
The reliability of travel
time significantly affects individual travelers’ decision-making behaviour and
hence in turn influences the overall travel demand at the macroscopic level. The travel time reliability
ratio (TTRR), defined to be the ratio of the value of travel time variability
to the value of travel time, is an important metric for measuring such reliability. In this paper, we first
show that the TTRR is closely related to a widely used risk measure in
financial economics, i.e. conditional value at risk (CVaR). Then based on the
connection between the TTRR and CVaR, we develop a nonparametric approach to
estimate the TTRR. In the literature, to compute the TTRR, it usually needs to assume
a specific statistical distribution for the travel time. This can produce a misleading
result when this assumption goes awry due to the potential complexity of travel
time distributions. Based on the relationship between the TTRR and CVaR, this
paper proposes a new nonparametric method, i.e. the kernel
density estimation method, to overcome this problem. We show that this new nonparametric
method is robust in terms that it does not depend on any assumptions about the
shape of the travel time distribution. The simulation studies demonstrate that the
proposed method outperforms the existing methods and substantially improves the
numerical accuracy. Finally, a practical example is used to illustrate the
proposed method
History
School
- Business and Economics
Department
- Business
Published in
Transportation Research Part B: Methodological: an International JournalVolume
130Pages
152-171Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This paper was accepted for publication in the journal Transportation Research Part B: Methodological: an International Journal and the definitive published version is available at https://doi.org/10.1016/j.trb.2019.10.009Acceptance date
2019-10-25Publication date
2019-11-12Copyright date
2019ISSN
0191-2615Publisher version
Language
- en
Depositor
Prof Baibing Li Deposit date: 12 November 2019Usage metrics
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