Single Intersection MPC Traffic Signal Control in Presence of Automated Vehicles

被引:0
作者
Liberati, Francesco [1 ]
Donsante, Manuel [1 ]
Tortorelli, Andrea [2 ]
机构
[1] Sapienza Univ Rome, Dept Comp Control & Management Engn Antonio Rubert, I-00185 Rome, Italy
[2] eCampus Univ, Fac Engn, Novedrate, Italy
关键词
Vehicle dynamics; Computational modeling; Adaptation models; Trajectory; Predictive control; Optimization; Mathematical models; Automated vehicles; model predictive control (MPC); traffic light (TL) control; traffic signal control; MODEL-PREDICTIVE CONTROL;
D O I
10.1109/TCST.2024.3449188
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article presents a model predictive control (MPC) approach for the management of traffic lights (TLs) at a single road intersection. The proposed controller incorporates a microscopic traffic model, capturing the position, velocity, and acceleration of every single vehicle at the intersection. This allows us to achieve a detailed modeling of the dynamics of the queues. The proposed controller can adapt to work in scenarios that go from one in which vehicles are manually controlled by the drivers, to one in which some or all of the vehicles are automatically driven. In the former scenario, the dynamics of the vehicles' variables are intended to mimic the drivers' behavior, in the latter ones (i.e., semi or fully autonomous driving), vehicles' variables are references to the automated vehicles, sent by the TL controller. Numerical simulations on a real intersection with realistic traffic characteristics are discussed and results in the scenarios from the manual one to the fully automated one are compared, evaluating the performance in terms of queue length and waiting times. It is shown how the proposed controller can significantly improve the management of the intersection, leading to less traffic.
引用
收藏
页码:1432 / 1446
页数:15
相关论文
共 56 条
[1]   Multi-intersection traffic signal control: A decentralized MPC-based approach [J].
Abbracciavento, Francesco ;
Zinnari, Francesco ;
Formentin, Simone ;
Bianchessi, Andrea G. ;
Savaresi, Sergio M. .
IFAC JOURNAL OF SYSTEMS AND CONTROL, 2023, 23
[2]   Real-time optimal traffic management in signal-controlled intersections: a receding-horizon approach [J].
Abbracciavento, Francesco ;
Zinnari, Francesco ;
Formentin, Simone ;
Bianchessi, Andrea G. ;
Savaresi, Sergio M. .
2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2021, :1947-1952
[3]   Limitations and Improvements of the Intelligent Driver Model (IDM) [J].
Albeaik, Saleh ;
Bayen, Alexandre ;
Chiri, Maria Teresa ;
Gong, Xiaoqian ;
Hayat, Amaury ;
Kardous, Nicolas ;
Keimer, Alexander ;
McQuade, Sean T. ;
Piccoli, Benedetto ;
You, Yiling .
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS, 2022, 21 (03) :1862-1892
[4]  
[Anonymous], GUROBI OPTIMIZER REF, P2021
[5]   Control of systems integrating logic, dynamics, and constraints [J].
Bemporad, A ;
Morari, M .
AUTOMATICA, 1999, 35 (03) :407-427
[6]   Julia: A Fresh Approach to Numerical Computing [J].
Bezanson, Jeff ;
Edelman, Alan ;
Karpinski, Stefan ;
Shah, Viral B. .
SIAM REVIEW, 2017, 59 (01) :65-98
[7]   Centralised and decentralised signal timing optimisation approaches for network traffic control [J].
Chow, Andy H. F. ;
Sha, Rui ;
Li, Shuai .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 113 :108-123
[8]   Traffic Signal Control Using End-to-End Off-Policy Deep Reinforcement Learning [J].
Chu, Kai-Fung ;
Lam, Albert Y. S. ;
Li, Victor O. K. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) :7184-7195
[9]   IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control [J].
Devailly, Francois-Xavier ;
Larocque, Denis ;
Charlin, Laurent .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) :7496-7507
[10]   JuMP: A Modeling Language for Mathematical Optimization [J].
Dunning, Iain ;
Huchette, Joey ;
Lubin, Miles .
SIAM REVIEW, 2017, 59 (02) :295-320