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.
Mitigating the impact of light rail on urban traffic networks using mixed‐integer linear programming
IET Intelligent Transport Systems ; 14 , 6 ; 523-533
2020-06-01
11 pages
Article (Journal)
Electronic Resource
English
light rail constraints , traffic control , light rail installation , linear programming , road traffic , scheduling , light rail schedule constraints , fixed‐time control schedules , mixed‐integer linear programming framework , urban traffic congestion , urban traffic networks , traffic engineering computing , resulting decrease , light rail presence , optimisation , street‐level , inexpensive public transit options , conventional traffic networks , adaptive signal control method , increase worldwide , 58.7% versus optimal fixed‐time control , integer programming , road traffic control , authors study , optimised traffic signal control , conventional vehicle traffic capacity
Mitigating the impact of light rail on urban traffic networks using mixed-integer linear programming
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