Personalized Menu Optimization with Preference Updater: A Boston Case Study

Xiang Song*, Mazen Danaf, Bilge Atasoy, Moshe Ben-Akiva

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)


This paper presents a personalized menu optimization model with preference updater in the context of an innovative Smart Mobility system that offers a personalized menu of travel options with incentives for each incoming traveler in real time. This Smart Mobility system can serve as a major travel demand management system that encourages energy-efficient travel options. The personalized menu optimization is built on a logit mixture model that captures each individual traveler’s choice behavior. The personalized menu optimization model is enhanced with a preference updater that can update the estimates of individual traveler’s preference parameters when new choice data is received. To illustrate the advantages of the proposed methodology, a case study is presented based on real travelers and trips in the greater Boston area from the Massachusetts Travel Survey data. The case study consists of two parts. In the first part, the personalized menu optimization with preference updater is tested in a setting where the travelers are new to the system and their preferences are updated through preference updater. A comparative analysis of the performance of the proposed method with preference updater is presented against the method without preference updater. In the second part, the benefit of using individual level preference parameters instead of population level preference parameters in the personalized menu optimization model is analyzed. The case study shows that the proposed method can outperform the hit rates of its two counterparts.

Original languageEnglish
Pages (from-to)599-607
JournalTransportation Research Record
Issue number8
Publication statusPublished - 2018
Externally publishedYes


Dive into the research topics of 'Personalized Menu Optimization with Preference Updater: A Boston Case Study'. Together they form a unique fingerprint.

Cite this