Modelling travel time distribution under various uncertainties on Hanshin expressway of Japan

• Published in: European transport research review Vol. 6; no. 1; pp. 85 - 92
• Main Authors: Chalumuri, Ravi Sekhar, Yasuo, Asakura
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2014; Springer Nature B.V
• Subjects: Automotive Engineering; Civil Engineering; Data collection; Distribution; Engineering; Expressways; Mathematical models; Original Paper; Rain; Regional/Spatial Science; Regression analysis; Response surfaces; Roads & highways; Sensors; Stochasticity; Time; Traffic; Traffic accidents & safety; Traffic engineering; Traffic flow; Transportation; Transportation planning; Travel; Uncertainty; Variables; Japan; Hanshin expressway; Stochastic response surface method; Travel time distribution
• ISSN: 1867-0717; 1866-8887
• DOI: 10.1007/s12544-013-0111-3

Purpose Studying travel time distribution or variability in travel time is very much useful in travel time reliability studies of transportation system. The properties of this distribution are described by various uncertainties which are derived from supply side, demand side and other external factors of any road network. Method Present study investigates the development of stochastic response surface of travel time variation under uncertain factors of traffic volume and intensity of rain fall by using Stochastic Response Surface Method (SRSM). Analysis and Results This model was applied to a section of Kobe Route (Nishnomiya to Awaza), Hanshin expressway, Japan. Besides hourly traffic volume data, incident data for the entire year 2006 were collected and multiple linear regression analysis for entire year data was initially performed to know the functional and significance relation between the input and output variable. Further SRSM analysis has been carried out for working days data. Results shows that travel time distribution obtained using SRSM model is better than distribution obtained by the regression model. Conclusion It was observed from the results that SRSM model is efficient for analyzing the stochastic relation between the response variable and uncertain explanatory variables.