Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control

Urban traffc congestion is on the increase worldwide; therefore, it is critical to maximize the capacity and throughput of the existing road infrastructure with optimized traffc signal control. For that purpose, this paper builds on the body of work in mixed integer linear programming (MILP) approac...

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Main Authors: Guilliard, Iain, Sanner, Scott, Trevizan, Felipe W., Williams, Brian C
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Published: Transportation Research Board 2018
Online Access:http://hdl.handle.net/1721.1/116302
https://orcid.org/0000-0002-1057-3940
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author Guilliard, Iain
Sanner, Scott
Trevizan, Felipe W.
Williams, Brian C
author2 Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
author_facet Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Guilliard, Iain
Sanner, Scott
Trevizan, Felipe W.
Williams, Brian C
author_sort Guilliard, Iain
collection MIT
description Urban traffc congestion is on the increase worldwide; therefore, it is critical to maximize the capacity and throughput of the existing road infrastructure with optimized traffc signal control. For that purpose, this paper builds on the body of work in mixed integer linear programming (MILP) approaches that attempt to optimize traffc signal control jointly over an entire traffc network and specifcally on improving the scalability of these methods for large numbers of intersections. The primary insight in this work stems from the fact that MILP-based approaches to traffc control used in a receding horizon control manner (that replan at fxed time intervals) need to compute high-fdelity control policies only for the early stages of the signal plan. Therefore, coarser time steps can be used to see over a long horizon to adapt preemptively to distant platoons and other predicted long-term changes in traffc?ows. To that end, this paper contributes the queue transmission model (QTM), which blends elements of cell-based and link-based modeling approaches to enable a nonhomogeneous time MILP formulation of traffc signal control. Experimentation is then carried out with this novel QTM-based MILP control in a range of traffc networks, and it is demonstrated that the nonhomogeneous MILP formulation achieves (a) substantially lower delay solutions, (b) improved per vehicle delay distributions, and (c) more optimal travel times over a longer horizon in comparison with the homogeneous MILP formulation with the same number of binary and continuous variables.
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spelling mit-1721.1/1163022022-09-26T12:44:23Z Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control Guilliard, Iain Sanner, Scott Trevizan, Felipe W. Williams, Brian C Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Williams, Brian C Urban traffc congestion is on the increase worldwide; therefore, it is critical to maximize the capacity and throughput of the existing road infrastructure with optimized traffc signal control. For that purpose, this paper builds on the body of work in mixed integer linear programming (MILP) approaches that attempt to optimize traffc signal control jointly over an entire traffc network and specifcally on improving the scalability of these methods for large numbers of intersections. The primary insight in this work stems from the fact that MILP-based approaches to traffc control used in a receding horizon control manner (that replan at fxed time intervals) need to compute high-fdelity control policies only for the early stages of the signal plan. Therefore, coarser time steps can be used to see over a long horizon to adapt preemptively to distant platoons and other predicted long-term changes in traffc?ows. To that end, this paper contributes the queue transmission model (QTM), which blends elements of cell-based and link-based modeling approaches to enable a nonhomogeneous time MILP formulation of traffc signal control. Experimentation is then carried out with this novel QTM-based MILP control in a range of traffc networks, and it is demonstrated that the nonhomogeneous MILP formulation achieves (a) substantially lower delay solutions, (b) improved per vehicle delay distributions, and (c) more optimal travel times over a longer horizon in comparison with the homogeneous MILP formulation with the same number of binary and continuous variables. NICTA NSW Trade & Investment 2018-06-14T13:32:58Z 2018-06-14T13:32:58Z 2016 2018-04-18T12:15:21Z Article http://purl.org/eprint/type/JournalArticle 0361-1981 http://hdl.handle.net/1721.1/116302 Guilliard, Iain, et al. “Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control.” Transportation Research Record: Journal of the Transportation Research Board, vol. 2595, Jan. 2016, pp. 128–38. https://orcid.org/0000-0002-1057-3940 http://dx.doi.org/10.3141/2595-14 Transportation Research Record: Journal of the Transportation Research Board Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Transportation Research Board Other univ. web domain
spellingShingle Guilliard, Iain
Sanner, Scott
Trevizan, Felipe W.
Williams, Brian C
Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title_full Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title_fullStr Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title_full_unstemmed Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title_short Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control
title_sort nonhomogeneous time mixed integer linear programming formulation for traffic signal control
url http://hdl.handle.net/1721.1/116302
https://orcid.org/0000-0002-1057-3940
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