Online discrete choice models: Applications in personalized recommendations

Danaf, Mazen; Bécker, Félix; Song, Xiang; Atasoy, Bilge; Ben‐Akiva, Moshe

doi:10.1016/j.dss.2019.02.003

Cited by 47 publications

(27 citation statements)

References 23 publications

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“…The travel options include choosing between using a private taxi service, shared taxi service, or minibus (multiple passengers with fixed routes but flexible schedules). Danaf et al (2019) provide a good overview of how behavioural models can be applied in real time to generate customised recommendations which facilitate the usage of an integrated fixed and flexible public transport system. Notwithstanding, these studies did not determine the service parameters of on-demand service such as fleet size.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Narayan

Cats

Oort

et al. 2020

Transportmetrica A: Transport Science

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The recent emergence of innovative mobility solutions is changing the mobility landscape in urban areas. However, it remains unknown how the combined operation of private and pooled on-demand services affect service performance and the required dimensioning of the fleet size for such services. This study develops a model to determine the fleet size of an on-demand system offering private service and pooled service, where the demand for these services is an outcome of modal choices. We investigate the fleet size required when taking either the perspective of Transit Planning Authority (Agency) or Service Provider (Operator). The model is implemented for the network of Amsterdam North. Results show that the objectives of Agency and Operator yield different total fleet sizes with the Agency requiring a larger fleet than the Operator and that the optimal scenario for the Agency would be the one where only private on-demand service is offered.

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Section: Literature Reviewmentioning

confidence: 99%

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Narayan

Cats

Oort

et al. 2020

Transportmetrica A: Transport Science

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“…This update could be equivalently done by running Steps 4 and 5 in Gibbs sampling in Equations 13 and 14. This is introduced in Danaf et al (27).…”

Section: Parameter Personalizationmentioning

confidence: 99%

“…This creates a natural opportunity for determining individual preferences as personal travel history can easily be collected. There is also an inherent incentive for doing so, as tailored services (or products) can be then provided to different travelers with varied preferences ( 27 ) to maximize profit or welfare.…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Choice Model for Managed Lane Travel Behavior

Xie

Zhang

Akkinepally

et al. 2020

Transportation Research Record

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This paper presents a methodology for enhancing discrete choice models for managed lane travel behavior with personal trip history. We refer to this process as personalization and the enhanced model as a personalized choice model. With the objective of better understanding managed lane choices and improving the model’s prediction capability, personalization was carried out at two levels. First, we used each traveler’s habits and travel experiences before each trip for constructing a set of explanatory variables that could be used with any model structure. Second, under a logit mixture framework, the distribution of random parameters was updated with Bayesian inference according to personal trip history. The structure of the parameter distribution explicitly considered preference variations across individuals (interpersonal heterogeneity), as well as preference variations across trips performed by the same individual (intrapersonal heterogeneity). The proposed methodology is especially relevant for modeling revealed preference (RP) data from automatic vehicle identification sensors, for which limited socioeconomic characteristics of travelers are available. An empirical study was conducted on an operational managed lane corridor near Dallas/Fort Worth Airport in Texas. Available trip records over a 5-month period were utilized. A hierarchical Bayes estimator was adopted for efficient model estimation. The results suggest significant inter- and intrapersonal heterogeneity and that the proposed personalization method improves the model’s explanatory power and prediction capability. To the best of our knowledge, this paper represents the first introduction of personalization in managed lane choice behavior modeling and the first attempt to estimate intrapersonal heterogeneity with RP data.

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“…The estimation and update of preferences are implemented through an innovative preference updater which is exogenous to the recommendation. Preference updater uses the Hierarchical Bayes estimation procedure for logit mixture as developed by Danaf et al ( 21 ). The update procedure includes an offline update (estimates individual-level as well as population-level parameters) and an online update (estimates individual-level parameters using previous offline estimates of population-level parameters and leaves them unchanged) ( 21 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Preference updater uses the Hierarchical Bayes estimation procedure for logit mixture as developed by Danaf et al ( 21 ). The update procedure includes an offline update (estimates individual-level as well as population-level parameters) and an online update (estimates individual-level parameters using previous offline estimates of population-level parameters and leaves them unchanged) ( 21 ). The online update is quite efficient as it does not re-estimate population level parameters and therefore it is suitable for real-time recommendation models such as User Optimization in Tripod.…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Menu Optimization with Preference Updater: A Boston Case Study

Song

Danaf

Atasoy

et al. 2018

Transportation Research Record

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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.

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Online discrete choice models: Applications in personalized recommendations

Cited by 47 publications

References 23 publications

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Fleet size determination for a mixed private and pooled on-demand system with elastic demand

Personalized Choice Model for Managed Lane Travel Behavior

Personalized Menu Optimization with Preference Updater: A Boston Case Study

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