Free-hand gestural input is essential for emerging user interactions. We present Aili, a table lamp reconstructing a 3D hand skeleton in real time, requiring neither cameras nor on-body sensing devices. Aili consists of an LED panel in a lampshade and a few low-cost photodiodes embedded in the lamp base. To reconstruct a hand skeleton, Aili combines 2D binary blockage maps from vantage points of different photodiodes, which describe whether a hand blocks light rays from individual LEDs to all photodiodes. Empowering a table lamp with sensing capability, Aili can be seamlessly integrated into the existing environment. Relying on such low-level cues, Aili entails lightweight computation and is inherently privacy-preserving. We build and evaluate an Aili prototype. Results show that Aili’s algorithm reconstructs a hand pose within 7.2 ms on average, with 10.2° mean angular deviation and 2.5-mm mean translation deviation in comparison to Leap Motion. We also conduct user studies to examine the privacy issues of Leap Motion and solicit feedback on Aili’s privacy protection. We conclude by demonstrating various interaction applications Aili enables.

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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This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models under one of two extreme assumptions: either an unrestricted full covariance matrix (allowing correlations between all random coefficients), or a restricted diagonal matrix (allowing no correlations at all). Our objective is to find optimal subsets of correlated coefficients for which we estimate covariances. We propose a new estimator, called MISC, that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix. The optimal sparsity level of the covariance matrix is determined using out-of-sample validation. We demonstrate the ability of MISC to correctly recover the true covariance structure from synthetic data. In an empirical illustration using a stated preference survey on modes of transportation, we use MISC to obtain a sparse covariance matrix indicating how preferences for attributes are related to one another.