Avishai Zagoury
Software Engineer at Google Research
Authored Publications
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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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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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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)
Preview abstract
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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Systematic Data Driven Detection of Unintentional Changes in Traffic Light Plans
Dan Karliner
Eliav Buchnik
Shai Ferster
Tom Kalvari
Omer Litov
Nitzan Tur
Danny Veikherman
Jack Haddad
2024
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Traffic light plans determine the time allocated to each movement within an intersection. The plan has a high impact on vehicle travel performance, such as on the average delay time or the probability of stopping at the intersection. Traffic engineers of a city control its traffic lights and can make changes in their plans to improve traffic performance. As it is not always easy to predict the impact of such transitions, they can also be detrimental. We present an experimental study of real transitions in traffic plans in 10 cities with a total of over 9900 intersections within a time period of over 40 days. We focus on changes in the cycle time of plans that have a major influence on performance metrics such as delay. We compare the overall impact of such transitions and dive into several of them through a careful analysis. Interestingly, we indicate that many of the changes result in higher delay. To the best of our knowledge, our study is one of the largest experimental studies of traffic conditions in recent years.
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Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. Here we study using such LMs to fill in entities in comparative questions, like “Which country is older, India or ___?”—i.e., we study the ability of neural LMs to ask (not answer) reasonable questions. We show that accuracy in this fill-in-the-blank task is well-correlated with human judgements of whether a question is reasonable, and that these models can be trained to achieve nearly human-level performance in completing comparative questions in three different sub-domains. However, analysis shows that what they learn fails to model any sort of broad notion of which entities are semantically comparable or similar—instead the trained models are very domain-specific, and performance is highly correlated with co-occurrences between specific entities observed in the training set. This is true both for models that are pre-trained on general text corpora, as well as models trained on a large corpus of comparison questions. Our study thus reinforces recent results on the difficulty of making claims about a deep model’s world knowledge or linguistic competence based on performance on specific benchmark problems. We make our evaluation datasets publicly available to foster future research.
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Given the ubiquity of negative campaigning in recent political elections, we find it important to study its properties from a computational perspective. To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. When the goal is constructive (making a preferred candidate win), we prove that finding such a demotion strategy is easy for Plurality and Veto, while generally hard for t-approval and Borda. We also provide a t-factor approximation for t-approval for every fixed t, and a 3-factor approximation algorithm for Borda. Interestingly enough - following recent trends in political science that show that the effectiveness of negative campaigning depends on the type of candidate and demographic - when assigning varying prices to different possible demotion operations, we are able to provide inapproximability results. When the goal is destructive (making the leading opponent lose), we show that the problem is easy for a broad class of scoring rules.
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