Volume 24, Issue 3 (Autumn 2019)                   JPBUD 2019, 24(3): 111-132 | Back to browse issues page

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Mamdoohi A R, Delfan Azari A, Alomoradi M. Estimating Bus Travel Time Using Survival Models. JPBUD 2019; 24 (3) :111-132
URL: http://jpbud.ir/article-1-1891-en.html
1- Associate Professor, Faculty of Environment and Civil Engineering, Tarbiat Modares University, Tehran, Iran , armamdoohi@modares.ac.ir
2- M.A. Student in Economics, Institute for Management and Planning Studies (IMPS), Tehran, Iran.
3- Lecturer, Institute for Management and Planning Studies, Tehran, Iran.
Abstract:   (2785 Views)
 The prevailing model in the studies that estimate bus travel time is the linear regression which assumes the limit of the normal distribution for all observations. Besides, survival models can calculate that the probability of an event can change over time. Thus, examining event probabilities that change over time is ideal for risky basic models such as survival ones. Although these kinds of models are used less in the research of bus travel time, in this study Accelerated Failure Time (AFT) survival models and linear regression models are compared in the form of two modeling approaches, link-based, and section-based. As for modeling the Automated Vehicle Location (AVL), data of 32 buses in line number 313 in Tehran (from Sepah Sq. to Enqelab Sq.) is used, including the information for one week for May, August, and November 2015. According to the results, the accuracy of survival models is better than the linear regression model in both modeling approaches. Furthermore, the performance of the linear regression model is unfavorable for both observations of short (less than 100 seconds) and long (more than 900 seconds) travel time. In addition, the particular lane that has been built in the opposite direction in this route reduces the bus travel time by an average of about 15.7 percent.
Full-Text [PDF 1908 kb]   (796 Downloads)    
Type of Study: Research | Subject: economic development, regional economics and growth
Received: 2020/04/25 | Accepted: 2020/07/7 | ePublished: 2020/09/14

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