Avinatan Hassidim
Authored Publications
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Fine-grained Measurement of Vehicle Delay Fairness
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Danny Veikherman
Ron Tsibulsky
Shai Ferster
2025
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Optimizing signal timing in traffic lights helps to improve traffic flow and reduce emissions through reducing delays. At intersections, vehicles from different movements observe different delays impacted by the traffic light plan. This paper analyzes delay fairness among various vehicles at intersections. We refer to three cities: Rio de Janeiro, Hamburg and Seattle with a total number of over 5100 intersections. We present an intuitive methodology to compute delay fairness based on Gini index, a common fairness measure in economics. We evaluate the fairness based on real traffic data and provide insights on the relationship of fairness with day hours and traffic demand. We also examine real changes in traffic light plans that occurred in practice to check whether improving delay is often aligned with increasing fairness.
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Towards Conversational AI for Disease Management
Khaled Saab
David Stutz
Kavita Kulkarni
Sara Mahdavi
Joelle Barral
James Manyika
Ryutaro Tanno
Adam Rodman
arXiv (2025)
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While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic response, and safe medication prescription - remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini's long-context capabilities, combining in-context retrieval with structured reasoning to align its output with relevant and up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialist physicians and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding of management plans in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies (US, UK) and validated by board-certified pharmacists. While AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management.
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An Empirical Study of Time of Day Breakpoints in Traffic Light Plans
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Danny Veikherman
Shai Ferster
2025
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Fixed time strategy is a common approach in signal traffic control in which signal plans are simple and periodic, enjoying easy implementation without detection mechanisms. A traffic light is associated with several daily plans, each applied to several consecutive hours. Time-of-day breakpoints (TODs) refer to the times over the day in which the plan is changed. TODs are often selected based on traffic, aiming to divide the day into groups of consecutive hours with similar traffic characteristics within each group of hours. We present a methodology to study time-of-day breakpoints in practice. We use this methodology to estimate and analyze time-of-day breakpoints in the city of Rio de Janeiro, Brazil based on traffic properties derived from traffic trajectories. Our study examines over 900 of the city intersections. We refer to properties such as the number of daily plans and the times by which plans start. We also provide traffic-aware insights on the potential improvement in the selection of TODs and identify key intersections where adjusting TODs could reduce average delay times. We identify potential improvements in over 8% of the examined intersections. These findings provide valuable insights for traffic engineers seeking to optimize signal timing.
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LLM-based Lossless Text Simplification and its Effect on User Comprehension and Cognitive Load
Theo Guidroz
Diego Ardila
Jimmy Li
Adam Mansour
Paul Jhun
Nina Gonzalez
Xiang Ji
Mike Sanchez
Miguel Ángel Garrido
Divyansh Choudhary
Jay Hartford
Georgina Xu
Henry Serrano
Yifan Wang
Jeff Shaffer
Eric (Yifan) Cao
Sho Fujiwara
Peggy Bui
arXiv (2025)
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Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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Day-of-the-week Awareness in Time of Day Breakpoints for Traffic Light Plans
Eliav Buchnik
Shai Ferster
Tom Kalvari
Ron Tsibulsky
Danny Veikherman
Jack Haddad
2025
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Time-of-day breakpoints (TODs) refer to the times over the day in which the plan of a traffic light is changed. Traditionally, TODs are selected jointly for all weekdays (Monday-Friday), typically with additional TODs dedicated to weekends. In this paper, we present an alternative approach motivated by traffic characteristics that can differ among the weekdays Monday-Friday and consider TODs which are day-of-the-week aware. The traffic-aware approach studies similarities among days and computes TODs that can be shared among days with similar characteristics but can also have other forms for weekdays with unique characteristics. Based on traffic properties derived from anonymized trajectories, we apply the new methodology to compute time-of-day breakpoints that are day-of-the-week aware in the city of Rio de Janeiro, Brazil and estimate the impact of the new methodology.
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Study of Arterials in the City of Rio de Janeiro for Traffic Coordination
Eliav Buchnik
Danny Veikherman
Dan Karliner
Tom Kalvari
Shai Ferster
Ron Tsibulsky
Jack Haddad
2025
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Urban traffic congestion is a growing challenge, and optimizing signal timing strategies is crucial for improving traffic flow and reducing emissions. The coordination of signalized intersections improves both traffic operations and environmental aspects. Coordination is particularly important along arterials, sequences of signalized intersections that serve as the primary routes and carry a high volume of traffic. In this paper we analyze real data from the city of Rio de Janeiro to study properties of arterials. We refer to their length, the distance between intersections and to the properties of the traffic light plans such as cycle time. We then study their in practice level of coordination in terms of number of stops and their common locations along the arterials. We dive into particular arterials and provide insights that can be useful for efficient design of arterials in additional cities. Based on the analysis, we show how simple traffic properties can indicate the potential upon coordinating two adjacent intersections as part of an arterial in improving traffic performance.
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Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning
Amy Wang
Roma Ruparel
Paul Jhun
Julie Anne Seguin
Patricia Strachan
Renee Wong
2025
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Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), have demonstrated significant potential in clinical reasoning skills such as history-taking and differential diagnosis generation—critical aspects of medical education. This work explores how LLMs can augment medical curricula through interactive learning. We conducted a participatory design process with medical students, residents and medical education experts to co-create an AI-powered tutor prototype for clinical reasoning. As part of the co-design process, we conducted a qualitative user study, investigating learning needs and practices via interviews, and conducting concept evaluations through interactions with the prototype. Findings highlight the challenges learners face in transitioning from theoretical knowledge to practical application, and how an AI tutor can provide personalized practice and feedback. We conclude with design considerations, emphasizing the importance of context-specific knowledge and emulating positive preceptor traits, to guide the development of AI tools for medical education.
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Probe-Based Study of Traffic Variability for the Design of Traffic Light Plans
Eliav Buchnik
Shai Ferster
Tom Kalvari
Dan Karliner
Omer Litov
Nitzan Tur
Danny Veikherman
Jack Haddad
COMSNETS 2024, https://www.comsnets.org/ (2024)
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Computing efficient traffic signal plans is often based on the amount of traffic in an intersection, its distribution over the various intersection movements and hours as well as on performance metrics such as traffic delay. In their simple and typical form, plans are fixed in the same hour over weekdays. This allows low operation costs without the necessity for traffic detection and monitoring tools. A critical factor in the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. In this paper, we study traffic variability and propose simple metrics to measure it based on traffic volume and traffic delay. We propose an automatic probe data-based method, for city-wide estimation of traffic variability. We discuss how such measures can be used for signal planning such as an indication of which intersections can benefit from dynamic but expensive traffic detection tools or in selecting plan resolution. Likewise, we discuss various methods to mitigate the impact of such variability. We demonstrate the framework based on real traffic statistics to study the traffic variability in the city of Haifa along its 162 intersections.
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Estimating Daily Start Times of Periodic Traffic Light Plans from Traffic Trajectories
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Omer Litov
Danny Veikherman
Shai Ferster
Nitzan Tur
2024
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In recent years, the wealth of available vehicle location data from connected vehicles, cell phones, and navigation systems has been introduced. This data can be used to improve the existing transportation network in various ways. Among the most promising approaches is traffic light optimization. Traffic light optimization has the potential to reduce traffic congestion, air pollution and GHG emissions. The first step in such optimization is the understanding of the existing traffic light plans. Such plans are periodic but, in practice, often start every day at arbitrary times, making it hard to align traffic trajectories from various days toward the analysis of the plan. We provide an estimation model for estimating the daily start time of periodic plans of traffic lights. The study is inspired by real-world data provided, for instance, by navigation applications. We analyze the accuracy of such computations as a function of the characteristics of the sampled traffic and the length of the evaluated time period.
from the complete traffic and potential noise in the samples.
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QUANTITATIVE APPROACH FOR COORDINATION, AT SCALE, OF SIGNALIZED 2 INTERSECTION PAIRS
Jack Haddad
Nitzan Tur
Danny Veikherman
Eliav Buchnik
Shai Ferster
Tom Kalvari
Dan Karliner
Omer Litov
2024
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The coordination of signalized intersections in urban cities improves both traffic operations and environmental aspects. Traffic signal coordination has a long history, where the impact of offset on delays and emissions at signalized intersections have been investigated through simulations and a limited number of experimental findings. Coordinating intersections is often justified by specific engineering requirements and judgment. However, as a consequence, many intersections in cities remain uncoordinated.
In this paper, we examine the potential benefits of coordinating signalized intersections at scale. Unlike previous studies, our analysis is based on aggregated anonymized probe data analysis and does not need to explicitly model traffic-oriented issues such as queue spillback and platoon dispersion. We follow a decentralized approach by considering intersection pairs, i.e. a system of two signalized intersections which can be spatially coupled, but have different cycle lengths. We introduce a new method for coordinating those signalized intersections. The method first evaluates the effect of different offsets on vehicle travel times and emissions. Then, it coordinates the two intersections by setting a common cycle and finding the optimal offset that minimizes emissions and travel times. We present the analysis for several case studies from real intersections at Jakarta, Rio de Janeiro, Kolkata, and Haifa. Finally, we evaluated our method by implementing it in a real experimental study at Jakarta. We collaborated with the city to implement the optimal offset that we had determined, and we compared the results before and after coordination.
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