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Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing.

Authors :
Chen, Peng
Tong, Rui
Lu, Guangquan
Wang, Yunpeng
Source :
Journal of Advanced Transportation. 5/7/2018, p1-13. 13p.
Publication Year :
2018

Abstract

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01976729
Database :
Academic Search Index
Journal :
Journal of Advanced Transportation
Publication Type :
Academic Journal
Accession number :
129476745
Full Text :
https://doi.org/10.1155/2018/3747632