Riccardo Iacobucci

Shared Autonomous Vehicles (SAV), or robotaxis, are expected to be commercially available within this decade. This new transport mode has the potential to revolutionize travel, offering a level of service comparable to traditional taxis with much lower prices. This may attract travelers currently using other modes, impacting the economic sustainability of public transport as well as car ownership levels. We investigate this potential demand using a scalable SAV simulation framework. We do not establish a future equilibrium considering the interaction between all users on a detailed road network, but establish the potential demand for a large metropolitan area. Travelers can choose between their current mode and the new SAV mode, with fare and waiting times which depend on real-time demand. For our input data we train a statistical model on a large transport survey from Germany for an urban region, allowing us to generate a large number of trips with realistic characteristics. We conduct a sensitivity analysis to study the effect of several key parameters on the modal shift. We find that SAVs can be attractive to many active mode and public transport users unless regulations are put in place. Our results also show that due to SAV fleet constraints, changes in incentives for travelers currently using cars may have significant consequences on the behavior of other travelers. We further calculate key economic indicators for the fleet, which can inform the discussion on the fleet size and fare level that operators are likely to choose when maximizing their own profit.

Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.

Background: The study of doctor-patient-computer interactions is a key research area for examining doctor-patient relationships; however, studying these interactions is costly and obtrusive as researchers usually set up complex mechanisms or intrude on consultations to collect, then manually analyze the data. Objective: We aimed to facilitate human-computer and human-human interaction research in clinics by providing a computational ethnography tool: an unobtrusive automatic classifier of screen gaze and dialogue combinations in doctor-patient-computer interactions. Methods: The classifier’s input is video taken by doctors using their computers' internal camera and microphone. By estimating the key points of the doctor's face and the presence of voice activity, we estimate the type of interaction that is taking place. The classification output of each video segment is 1 of 4 interaction classes: (1) screen gaze and dialogue, wherein the doctor is gazing at the computer screen while conversing with the patient; (2) dialogue, wherein the doctor is gazing away from the computer screen while conversing with the patient; (3) screen gaze, wherein the doctor is gazing at the computer screen without conversing with the patient; and (4) other, wherein no screen gaze or dialogue are detected. We evaluated the classifier using 30 minutes of video provided by 5 doctors simulating consultations in their clinics both in semi- and fully inclusive layouts. Results: The classifier achieved an overall accuracy of 0.83, a performance similar to that of a human coder. Similar to the human coder, the classifier was more accurate in fully inclusive layouts than in semi-inclusive layouts. Conclusions: The proposed classifier can be used by researchers, care providers, designers, medical educators, and others who are interested in exploring and answering questions related to screen gaze and dialogue in doctor-patient-computer interactions.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The study of doctor-patient-computer interactions is a key research area for examining doctor-patient relationships; however, studying these interactions is costly and obtrusive as researchers usually set up complex mechanisms or intrude on consultations to collect, then manually analyze the data. Objective: We aimed to facilitate human-computer and human-human interaction research in clinics by providing a computational ethnography tool: an unobtrusive automatic classifier of screen gaze and dialogue combinations in doctor-patient-computer interactions. Methods: The classifier’s input is video taken by doctors using their computers' internal camera and microphone. By estimating the key points of the doctor's face and the presence of voice activity, we estimate the type of interaction that is taking place. The classification output of each video segment is 1 of 4 interaction classes: (1) screen gaze and dialogue, wherein the doctor is gazing at the computer screen while conversing with the patient; (2) dialogue, wherein the doctor is gazing away from the computer screen while conversing with the patient; (3) screen gaze, wherein the doctor is gazing at the computer screen without conversing with the patient; and (4) other, wherein no screen gaze or dialogue are detected. We evaluated the classifier using 30 minutes of video provided by 5 doctors simulating consultations in their clinics both in semi- and fully inclusive layouts. Results: The classifier achieved an overall accuracy of 0.83, a performance similar to that of a human coder. Similar to the human coder, the classifier was more accurate in fully inclusive layouts than in semi-inclusive layouts. Conclusions: The proposed classifier can be used by researchers, care providers, designers, medical educators, and others who are interested in exploring and answering questions related to screen gaze and dialogue in doctor-patient-computer interactions.

Shared autonomous electric vehicles (SAEVs) are being introduced in pilot programs and they are expected to be commercially available by the next decade. In this work, we propose a methodology for the joint optimisation of vehicle charging, vehicle-to-grid (V2G) services and fleet rebalancing in mobility systems using SAEVs. The proposed model is implemented as a cascaded model predictive control (MPC) optimisation framework with two different timescales. The first MPC scheme, called energy layer, abstracts the fleet of SAEVs as an aggregate storage system for the sake of model scalability, and it optimises fleet charging and V2G services to minimise electricity cost over a long timescale (hours). The second MPC scheme, called transport layer, optimises short-term vehicle routing and relocation decisions to minimise customers’ waiting times while taking into account the charging constraints derived from the energy layer. A case study using transport and electricity price data for the city of Tokyo is used to validate the model. Results demonstrate that our approach is computationally scalable and it can be applied to large-scale scenarios. In addition, it allows to significantly reduce charging costs with limited impact on passengers’ waiting times

Shared autonomous electric vehicles (SAEV s), also known as autonomous mobility on demand systems, are expected to soon be commercially available. This work proposes a methodology for the optimization of SAEV charging taking into account optimized vehicles routing and rebalancing. The methodology presented is based on previous work expanded to include charge scheduling optimization. Our model deals with the different time frames at which transport service and charging have to be optimized with a model-predictive control optimization routine which is run in parallel at two different time scales. Vehicle charging is optimized over longer time scales to minimize waiting times for passengers and electricity costs. Routing and rebalancing is optimized at shorter time-scales to minimize waiting times for passengers, taking as charging constraints the results of the long-time-scale optimization. This approach allows the efficient optimization of both aspects of SAEV operation. The problem is solved as a mixed-integer linear program. A case study using real transport data for Tokyo is used to test the model, showing that the system can substantially cut charging costs while keeping passenger wait times low.

The introduction of shared autonomous electric vehicles (SAEVs), expected within the next decade, can transform the car into a service, accelerate electrification of the transport sector, and allow for large scale control of electric vehicle charging. In this work, we investigate the potential for this system to provide aggregated storage when combined with intermittent renewable energy sources. We develop a simulation methodology for the optimization of vehicle charging in the context of a virtual power plant or microgrid, with and without grid connection or distributed dispatchable generators. The model considers aggregate storage availability from vehicles based on transport patterns taking into account the necessary vehicle redistribution. We investigate the case of a grid-connected VPP with rooftop solar and the case of a isolated microgrid with solar, wind, and dispatchable generation. We conduct a comprehensive sensitivity analysis to study the effect of several parameters on the results for both cases.