Personalized Menu Optimization with Preference Updater: A Boston Case Study

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

doi:10.1177/0361198118758674

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…The menu is presented to the user (see Figure 2) with information about the recommended options and their tokens. The tokens for each alternative are calculated based on the energy savings from the expected choice and the menu is personalized according to the user's preferences, characteristic and network attributes (22). The user may select an option from the menu and use the Tripod app to navigate to the destination or opt out.…”

Section: Tripod Overviewmentioning

confidence: 99%

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

et al. 2019

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This paper presents a systematic way of understanding and modelling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet interconnected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with interconsumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modelling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based contextaware stated preference survey. Through model estimation, lower VOTs are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. The obtained results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobility services such as ridehailing services and the emerging MaaS (Mobility as a service).

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Section: Tripod Overviewmentioning

confidence: 99%

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

et al. 2019

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“…Then, the probability of choosing each alternative route can be calculated and the route choices can be predicted based on the probabilities, such as multinomial logit model, nested logit model, and path size logit ( 5 , 7 ). Lately, among the family of the discrete choice models, mixed logit models have drawn researchers’ attention for their capabilities to capture the preference heterogeneity by either estimating the preference distribution existing among the population or using a Bayesian approach to obtain individual-level preferences ( 8 – 10 ). In these research efforts, models were developed based on datasets of different sizes, and all need to put individuals’ data together to estimate model parameters, which could lead to privacy concerns in route navigation applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

Kweon

Sun

Park

2021

Transportation Research Record

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While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.

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“…It encourages energy efficient choices by presenting users with explicit and accurate energy cost information, notifications of accidents, and alternatives. The UO formulation and preference updater is described in more detail by Song et al ( 16 ).…”

Section: Overview Of Tripodmentioning

confidence: 99%

“…In addition, UE provides the information about the updated preferences of Tripod users to SO, for better predictions of SO strategies. For more details on the overall Tripod architecture and the UE optimization framework the reader is referred to Lima Azevedo et al ( 15 ) and Song et al ( 16 ), respectively.…”

Section: Overview Of Tripodmentioning

confidence: 99%

System-Level Optimization of Multi-Modal Transportation Networks for Energy Efficiency using Personalized Incentives: Formulation, Implementation, and Performance

Araldo

Gao

Seshadri

et al. 2019

Transportation Research Record

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The paper presents the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user’s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do so, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. SO is multi-modal, in that it considers private cars, public transit, walking, car pooling, and so forth. Moreover, it is dynamic, predictive, and personalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the framework with simulation results.

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Personalized Menu Optimization with Preference Updater: A Boston Case Study

Cited by 18 publications

References 24 publications

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

System-Level Optimization of Multi-Modal Transportation Networks for Energy Efficiency using Personalized Incentives: Formulation, Implementation, and Performance

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