Mitigating the impact of light rail on urban traffic networks using mixed-integer linear programming

• Published in: IET intelligent transport systems Vol. 14; no. 6; pp. 523 - 533
• Main Authors: Guilliard, Iain, Trevizan, Felipe, Sanner, Scott
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.06.2020
• Subjects: 58.7% versus optimal fixed‐time control; adaptive signal control method; authors study; conventional traffic networks; conventional vehicle traffic capacity; fixed‐time control schedules; increase worldwide; inexpensive public transit options; integer programming; light rail constraints; light rail installation; light rail presence; light rail schedule constraints; linear programming; mixed‐integer linear programming framework; optimisation; optimised traffic signal control; Research Article; resulting decrease; road traffic; road traffic control; scheduling; street‐level; traffic control; traffic engineering computing; urban traffic congestion; urban traffic networks; urban traffic congestion; integer programming; road traffic control; linear programming; 58.7% versus optimal fixed-time control; street-level; increase worldwide; optimisation; traffic control; optimised traffic signal control; scheduling; urban traffic networks; resulting decrease; authors study; road traffic; light rail presence; conventional traffic networks; adaptive signal control method; mixed-integer linear programming framework; traffic engineering computing; light rail constraints; inexpensive public transit options; conventional vehicle traffic capacity; light rail installation; fixed-time control schedules; light rail schedule constraints
• ISSN: 1751-956X; 1751-9578
• DOI: 10.1049/iet-its.2019.0277

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.