Prediction of Travel Time Reliability on Interstates Using Linear Quantile Mixed Models. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Xiaoxiao; Zhao, Mo; Appiah, Justice; Fontaine, Michael D.
• Is Part Of: Transportation research record. Volume 2677:Issue 2(2023)
• Page Start: 774
• Page End: 791
• Publication Date: 2023-02
• Subjects: data and data science -- urban transportation data and information systems -- data analysis; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221108380
• Languages: English
• ISSNs: 0361-1981

Abstract

Under the Moving Ahead for Progress in the 21st Century Act (MAP-21), state Departments of Transportation (DOTs) are responsible for reporting travel time reliability and also setting targets and showing progress toward those targets. To know how to improve travel time reliability and what to expect from investments in transportation infrastructure, state DOTs need a better understanding of the factors that affect travel time reliability and methods to predict future travel time reliability. This paper proposes linear quantile mixed models (LQMMs) to quantify travel time reliability impact factors and predict selected reliability measures (level of travel time reliability [LOTTR] and the 90th percentile) to address these needs. The method was demonstrated using probe vehicle data from interstate segments in Virginia that had been partitioned into approximately homogeneous clusters based on the similarity of their cumulative distribution functions (CDFs) of travel times. Using clustered data meant that LQMMs were only necessary for a limited number of clusters rather than for hundreds of individual segments, thus making the process more efficient and manageable. The LQMMs showed that frequencies of non-recurrent events, such as incidents and weather, were correlated with higher travel time percentiles. The prediction performance of LQMMs was compared with trend line predictions, a common method used in practice. The results showed that LQMMs significantly improved prediction accuracy.