Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is being studied to comprehensively break down the intrinsic barriers that existed in current solutions, to support the integration of connectable devices, the fusion of heterogeneous data, the composability of reusable functions, and the flexibility in their cooperations. To enable such a collaboration, this paper proposes a federated platform, called Future Mobility Sensing Advisor (FMSA), which can 1) manage the three elements through standardized interfaces separately and uniformly; 2) create a fully connected knowledge graph to orchestrate the three elements efficiently and effectively; 3) support the client-server interaction in centralized and federated modes to handle service requests and edge resources with various availability and accessibilities jointly and adaptively; and 4) accommodate various mobility services to foster harmonious and sustainable mobility tenderly and invisibly. Moreover, the efficiency and effectiveness of the platform are also tested through a performance evaluation, and a pilot supported at the Great Boston Area, respectively. As a result, it shows that FMSA can 1) achieve high performance by using the two interaction modes selectively, and 2) renovate smart mobility towards sustainability through personalized services that can measure user preferences and system objectives mutually.

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Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, we discuss cases where the estimates are consistent and those where they are not. We provide a theoretical explanation for this discrepancy and discuss the implications on the design of these systems and on model estimation. We conclude that endogeneity is not a concern when the likelihood function properly accounts for the data generation process. This can be achieved when the system is initialized exogenously and all the data are used in the estimation. In line with previous literature, Monte Carlo results suggest that, even when exogenous initialization is missing, empirical bias decreases with the number of choices per individual. We conclude by discussing the practical implications and extensions of this research.

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Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, the authors present a Monte Carlo experiment mimicking a recommender system for Mobility as a Service (MaaS) plans, showing cases where the estimates are consistent and those where they are not. In addition, they provide a theoretical explanation for this inconsistency and discuss the implications on the design of these systems and on model estimation. The authors conclude that endogeneity is not a concern when the likelihood function accounts for the data generation process and when all the data are used in the estimation. This can be achieved when the system is initialized exogenously and when this initialization is accounted for in the estimation.@en

This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing random taste variations among individuals and among choice situations (menus) for a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual-level and menu-specific. In the context of a recommender system, the estimation of these parameters is repeated periodically in an offline process in order to account for trends, such as changing market conditions. Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate the latest information from the users. This online update is computationally efficient which makes it feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various recommender systems that generate personalized recommendations ranging from transportation to e-commerce and online marketing, but is particularly useful when the attributes of the alternatives vary over time.

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Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively). However, the underlying model relies on strong assumptions on the inter- and intra-consumer mixing distributions; these distributions are assumed to be normal (or log-normal), and the intra-consumer covariance matrix is assumed to be the same for all individuals. This paper presents a latent class extension to the model and the estimator proposed by Becker et al. (2018) to account for flexible, semi-parametric mixing distributions. This relaxes the normality assumptions and allows different individuals to have different intra-consumer covariance matrices. The proposed model and the HB estimator are validated using real and synthetic data sets, and the models are evaluated using goodness-of-fit statistics and out-of-sample validation. Our results show that when the data comes from two or more distinct classes (with different population means and inter- and intra-consumer covariance matrices), this model results in a better fit and predictions compared to the single class model.

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The urban mobility landscape is witnessing widespread changes with the emergence of several disruptive technologies including mobility-as-a-service and automated vehicles. The convergence of these two developments in the form of automated mobility-on-demand (AMoD) services (i.e., a system of shared driverless taxis) is receiving growing interest from industry, governments and researchers worldwide as a promising solution to meet mobility needs in the future in a sustainable manner. However, there is a large degree of uncertainty surrounding the potential adoption of these systems, and their impact on individual travel/activity patterns and the transportation system as a whole. In this context, this paper attempts to gain insights into behavioral preferences and attitudes towards AMoD through a novel context-aware app-based stated preferences survey conducted in Singapore. The SP survey leverages a state-of-the-art smartphone-based platform (Future Mobility Sensing) and its ability to collect revealed preference (RP) and contextual data. Logit mixture models accounting for inter-person heterogeneity and panel effects were estimated on a sample of 2500 observations from 350 respondents. The results indicate the presence of heterogeneity in the valuation of in-vehicle travel time and out-of-vehicle travel time and significant differences across demographic categories. An analysis of price elasticity of demand for AMoD indicates a higher elasticity for AMoD taxi followed by AMoD shared19 taxi and AMOD mini-bus. The importance of modeling inertia in switching from the currently used mode is also highlighted. The results have important policy implications and the models have applications within detailed activity-based microsimulation models to examine the impact of AMoD in future scenarios. @en

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected 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 inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling 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 context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These 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 ride-hailing services and the emerging mobility as a service.@en

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-the-art smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative on-demand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current context-specific preferences in response to the new scenario.

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Estimating discrete choice models on panel data allows for the estimation of preference heterogeneity in the sample. While the Logit Mixture model with random parameters is mostly used to account for variation across individuals, preferences may also vary across different choice situations of the same individual. Up to this point, Logit Mixtures incorporating both inter- and intra-consumer heterogeneity are estimated with the classical Maximum Simulated Likelihood (MSL) procedure. The MSL procedure becomes computationally expensive with an increasing sample size and can be burdensome in the presence of a multi-modal likelihood function. We therefore propose a Hierarchical Bayes estimator for Logit Mixtures with both levels of heterogeneity. It builds on the Allenby-Train procedure, which considers only inter-consumer heterogeneity. To test the proposed procedures, we analyze how well the true patterns of heterogeneity are recovered in a simulation environment. Results from the Monte Carlo simulation suggest that falsely ignoring intra-consumer heterogeneity despite its presence in the data leads to biased estimates and a decreased goodness of fit. The latter is confirmed by a real-world example of explaining mode choices for GPS traces. We further show that the runtime of the proposed estimator is substantially faster than for the corresponding MSL estimator.

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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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