Open AccessMitigating the impact of light rail on urban traffic networks using mixed‐integer linear programming
Author(s) -
Guilliard Iain,
Trevizan Felipe,
Sanner Scott
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0277
Subject(s) - integer programming , offset (computer science) , linear programming , traffic signal , schedule , traffic congestion , computer science , public transport , control (management) , light rail , transport engineering , train , mathematical optimization , real time computing , operations research , engineering , mathematics , artificial intelligence , algorithm , geography , programming language , operating system , cartography
As urban traffic congestion is on the increase worldwide, many cities are increasingly looking to inexpensive public transit options such as light rail that operate at street‐level and require coordination with conventional traffic networks and signal control. A major concern in light rail installation is whether enough commuters will switch to it to offset the additional constraints it places on traffic signal control and the resulting decrease in conventional vehicle traffic capacity. In this study, the authors study this problem and ways to mitigate it through a novel model of optimised traffic signal control subject to light rail schedule constraints solved in a mixed‐integer linear programming (MILP) framework. The authors’ key results show that while this MILP approach provides a novel way to optimise fixed‐time control schedules subject to light rail constraints, it also enables a novel optimised adaptive signal control method that virtually nullifies the impact of the light rail presence, reducing average delay times in microsimulations by up to 58.7% versus optimal fixed‐time control.