Ori Rottenstreich
Ori Rottenstreich is an associate professor at the Department of Electrical and Computer Engineering and the Department of Computer Science at the Technion - Israel Institute of Technology. He is also a visiting researcher whose focus is on network algorithms and smart transportation. In 2015-2017 Ori was a Postdoctoral Research Fellow at the Department of Computer Science, Princeton University. He received the BSc in Computer Engineering (summa cum laude) and PhD degree from the Electrical Engineering department of the Technion, Israel. He was a recipient of the Rothschild Yad-Hanadiv postdoctoral fellowship and the Google Europe PhD Fellowship in Computer Networking as well as of the Israel Council for Higher Education Alon Fellowship. He also received the Best Paper Runner Up Award at the 2013 IEEE Infocom conference, the Best Paper Award at the 2017 ACM Symposium on SDN Research (SOSR), a Best Paper Award Candidate at the 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) and the Best Paper Award at the 2021 International Conference on Communication Systems and Networks (COMSNETS). He served as an editor for the IEEE Open Journal of the Communications Society as well as a guest editor for the IEEE Journal on Selected Areas in Communications (JSAC) and the IEEE Transactions on Network and Service Management (TNSM).
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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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Abstract—Traffic light plans determine the time allocated to each movement within an intersection. The plan has high influence on vehicle travel performance such as on the average delay time or the probability to stop in 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 changes, their potential impact can also be negative. We present an experimental study of real changes in traffic plans in 12 cities with a total of over 12000 intersections within a time period of over 40 days. We focus on changes of the cycle time of plans that highly impacted performance metrics such as delay. We compare the overall impact of such changes and dive into several of them through a careful analysis. To the best of our knowledge, our study is one of the largest in its scope among experimental studies of traffic conditions in recent years.
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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
Preview abstract
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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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 on the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. We refer to such similarity as the traffic stability of the intersection and define simple metrics to measure it based on traffic volume and traffic delay. In this paper, we propose an automatic probe data based method, for city-wide estimation of traffic stability. We discuss how such measures can be used for signal planning such as in selecting plan resolution or as an indication as which intersections can benefit from dynamic but expensive traffic detection tools. We also identify events of major changes in traffic characteristics of an intersection. We demonstrate the framework by using real traffic statistics to study the traffic stability in the city of Haifa along its 162 intersections. We study the impact of the time of day on the stability, detect major changes in traffic and find intersections with high and low stability.
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