Evaluating the influence of information provision (when and how) on route choice preferences of road users in Greater Orlando: Application of a regret minimization approach. (January 2021)

• Record Type: Journal Article
• Authors: Iraganaboina, Naveen Chandra; Bhowmik, Tanmoy; Yasmin, Shamsunnahar; Eluru, Naveen; Abdel-Aty, Mohamed A.
• Is Part Of: Transportation research. Volume 122(2021)
• Publication Date: 2021-01
• Subjects: Route choice -- Integrated active traffic management -- Stated Preference -- Regret Minimization; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102923
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Stated preference (SP) survey to understand route choice preferences in Greater Orlando Region. Random utility maximization and random regret minimization based mixed multinomial logit models are compared. Regret frameworks offered improved fit. Trade-offs associated with travel information provision (when and how) are estimated. Abstract: With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. While earlier research has examined route choice preferences in relation to travel time and travel cost (or toll), there is little guidance on the influence of information provision mechanisms. By accommodating for information provision attributes, the proposed research contributes to our understanding of the design of an active traffic management (ATM) system by quantitatively estimating the inherent trade-offs across the various attributes affecting route choice. Specifically, the research designs and elicits data using a web-based stated preference (SP) survey to understand road users' preferencesin the Greater Orlando Region, USA. In the empirical analysis, the data compiled is utilized to develop random utility maximization and random regret minimization based panel mixed multinomial logit models. Route choice behavior is modeled using a comprehensive set of exogenous variables including trip characteristics, roadway characteristics and traffic information characteristics. The model results are utilized to conduct a comprehensive trade-off analysis across various attributes for the two model frameworks. In this research effort, we also customize the trade-off computation for regret minimization model for accommodating variable interactions. The trade-offs results provide useful insights on travel information provision (when and how).