01623308

Component

https://doi.org/10.3141/2652-03

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

01628042

17-00984

English

pp 19–29

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309441933

Digital/other

Figures (3) ; References (37) ; Tables (4)

Automatic vehicle location; Fares; Public transit; Regression analysis; Smart cards; Travel time

Operations and Traffic Management; Planning and Forecasting; Public Transportation

TRIS, TRB, ATRI

Dec 8 2016 10:15AM

• Applications of Inferred Origins, Destinations, and Interchanges in Bus Service Planning
• Constructing Transit Origin–Destination Matrices with Spatial Clustering
• Estimating Fare Noninteraction and Evasion with Disaggregate Fare Transaction Data
• Estimation of Platform Waiting Time Distribution Considering Service Reliability Based on Smart Card Data and Performance Reports
• Exploratory Method for Practitioners Analyzing the Impact of Integrated Fare Structures in Decentralized Metropolitan Regions: A Toronto Region Case Study
• Fare System of Urban Public Transportation Services in Davao City, Philippines
• Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach
• Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data
• Modeling the Impact of Transit Fare Change on Passengers’ Accessibility
• Perceived Reality: Understanding the Relationship Between Customer Perceptions and Operational Characteristics
• Statistical Inference of Transit Passenger Boarding Strategies from Farecard Data
• The Youth Pass: A Study of the Conflicts Between Ideal and Practical Research Design in a Pilot Social Program
• Transit Trip Itinerary Inference with GTFS and Smartphone Data