TY - GEN
T1 - Estimating Daily Start Times of Periodic Traffic Light Plans from Traffic Trajectories
AU - Rottenstreich, Ori
AU - Kalvari, Tom
AU - Tur, Nitzan
AU - Buchnik, Eliav
AU - Ferster, Shai
AU - Karliner, Dan
AU - Litov, Omer
AU - Veikherman, Danny
AU - Zagoury, Avishai
AU - Haddad, Jack
AU - Emanuel, Dotan
AU - Hassidim, Avinatan
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85200573528&partnerID=8YFLogxK
U2 - https://doi.org/10.23919/ECC64448.2024.10590898
DO - https://doi.org/10.23919/ECC64448.2024.10590898
M3 - منشور من مؤتمر
T3 - 2024 European Control Conference, ECC 2024
SP - 3378
EP - 3385
BT - 2024 European Control Conference, ECC 2024
T2 - 2024 European Control Conference, ECC 2024
Y2 - 25 June 2024 through 28 June 2024
ER -