TY - GEN
T1 - Offset Selection for Bandwidth Maximization on Multiple Routes
AU - Mehr, Negar
AU - Sanselme, Marc
AU - Orr, Nitzan
AU - Horowitz, Roberto
AU - Gomes, Gabriel
N1 - Funding Information:
ACKNOWLEDGMENTS This work is supported by the National Science Foundation under Grants CPS 1446145 and CPS 1545116, and California Department of Transportation (Caltrans) under the Connected Corridors program.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - We consider the problem of offset selection for fixed-time signals in a network of arbitrary shape so as to increase the bandwidths that vehicles on multiple routes receive. Assuming that all signals have a common cycle, we utilize the concept of relative path offsets and formulate the problem of maximizing a weighted sum of path bandwidths. This leads to a nonlinear optimization problem. We demonstrate how this problem can be converted to a mixed-integer linear program; hence, providing a scalable computational framework. Our approach is in fact a generalization of a previous method in which the single arterial problem was found to be equivalent to a linear program, and is distinct from the traditional formulation as a mixed-integer program. We further show the practicality of our approach in a case study of a traffic network in San Diego, California.
AB - We consider the problem of offset selection for fixed-time signals in a network of arbitrary shape so as to increase the bandwidths that vehicles on multiple routes receive. Assuming that all signals have a common cycle, we utilize the concept of relative path offsets and formulate the problem of maximizing a weighted sum of path bandwidths. This leads to a nonlinear optimization problem. We demonstrate how this problem can be converted to a mixed-integer linear program; hence, providing a scalable computational framework. Our approach is in fact a generalization of a previous method in which the single arterial problem was found to be equivalent to a linear program, and is distinct from the traditional formulation as a mixed-integer program. We further show the practicality of our approach in a case study of a traffic network in San Diego, California.
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U2 - 10.23919/ACC.2018.8431660
DO - 10.23919/ACC.2018.8431660
M3 - Conference contribution
AN - SCOPUS:85052565299
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 6366
EP - 6371
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -