Development of an Adaptive Traffic Signal Control Framework for Urban Signalized Interchanges Based on Infrastructure Detectors and CAV Technologies
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
In this paper, we presented a novel adaptive traffic control strategy for urban signalized interchanges based on traditional sensors and emerging connected and automated vehicle (CAV) technologies. The signalized interchanges in this paper refer to those controlled by one traffic signal controller while vehicles must cross two or more stop lines to cross. Examples included diamond interchanges (DIs), diverging diamond interchanges (DDIs), and single-point urban interchanges (SPUIs). With the expansion of urban areas, such interchanges are increasingly common and often become mobility bottlenecks. The traffic signal optimization in this paper was derived from the cumulative vehicle counting curves ( curves). An assumption of the curves for control delay estimation is that vehicles are no longer restricted once they cross the stop line. However, at a signalized interchange, vehicles may stop multiple times before completely cross. This situation cannot be effectively reflected with the standard cumulative vehicle counting curves. The phasing sequence is also challenging due to the limited space within the interchange. To address these issues, we proposed a new adaptive traffic control framework based on a linear traffic control model, referred to as a phase-time network. The objective of this framework was to dynamically fine-tune control splits and optimize the phasing sequence according to the vehicle arrival counts (from infrastructure sensors) and turning movement ratios (from CAV technologies). The optimization problem was first formulated into a mixed-integer linear programming (MILP) formulation and validated through offline examples. Then, an online search algorithm was presented and evaluated within a microscopic traffic simulation environment. The proposed MILP formulation and algorithm were assessed in both offline and online experiments. The results of all numerical experiments validated the formulation and show promise for real-world implementations.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. All the GAMS codes for the MILP formulation of Experiments I, II, and III and Python code for computing efficiency analysis can be downloaded at https://github.com/pflee2002/Adaptive-signalized-snterchange-Control.
Acknowledgments
This research is partially supported by the project “Exploring a Novel Public-Private-Partnership Data Sharing Policy through a Collaborative Arterial Traffic Management System” sponsored by the USDOT UTC Center, Center for Transportation Equity, Decisions, and Dollars (CTEDD) and the project “Using LIDAR Sensors to Study Pedestrian Behaviors and Safety Improvement at Signalized Intersections” sponsored by the USDOT national UTC Center, National Institute for Transportation and Communities (NITC). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They do not necessarily reflect the official views or policies of the aforementioned organizations, nor do the contents constitute a standard, specification, or regulation of these organizations.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 22, 2021
Accepted: Dec 1, 2021
Published online: Jan 24, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 24, 2022
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