2022 journal article
An Enhanced Cell Transmission Model for Multi-Class Signal Control
IEEE Transactions on Intelligent Transportation Systems, 23(8), 11215–11226.
author keywords: Automobiles; Optimization; Process control; Linear programming; Transportation; Real-time systems; Loading; Multi-class cell transmission model; transit signal priority; signal timing optimization; traffic light control
TL;DR:
Constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the multi-class CTM are presented.
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UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science; OpenAlex)
Source: ORCID
Added: August 11, 2022
Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.