An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks

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An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks. / Han, Yu; Hegyi, Andreas; Yuan, Yufei; Roncoli, Claudio; Hoogendoorn, Serge.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 10, 01.10.2019, p. 3647-3660.

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@article{f350aa9a670142349e1015be06626b36,
title = "An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks",
abstract = "This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the LQMPC has a linear quadratic formulation that can be solved quickly, which is beneficial for a real-time application. However, the existing LQMPC approach does not preserve OD relations and thus may send traffic to wrong destinations. This problem is tackled by a heuristic method presented is this paper. We present two macroscopic models: 1) a non-linear route-specific model which keeps track of traffic dynamics for each OD pair and 2) a linear model that aggregates all route traffic states, which can be embedded into the LQMPC framework. The route-specific model predicts traffic dynamics and provides information to the LQMPC before the optimization and evaluates the optimal solutions after the optimization. The information obtained from the route-specific model is formulated as constraints in the LQMPC to narrow the solution space and exclude unrealistic solutions that would lead to flows that are inconsistent with the OD relations. The extended LQMPC approach is tested in a synthetic network with multiple bottlenecks. The simulation of the LQMPC approach achieves a total time spent close to the system optimum, and the computation time remains tractable.",
keywords = "Computational modeling, linear model, Merging, Model predictive control, Optimization, Predictive control, Predictive models, Roads, route guidance, Routing, signal control, spillback.",
author = "Yu Han and Andreas Hegyi and Yufei Yuan and Claudio Roncoli and Serge Hoogendoorn",
year = "2019",
month = "10",
day = "1",
doi = "10.1109/TITS.2018.2877259",
language = "English",
volume = "20",
pages = "3647--3660",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
number = "10",

}

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TY - JOUR

T1 - An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks

AU - Han, Yu

AU - Hegyi, Andreas

AU - Yuan, Yufei

AU - Roncoli, Claudio

AU - Hoogendoorn, Serge

PY - 2019/10/1

Y1 - 2019/10/1

N2 - This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the LQMPC has a linear quadratic formulation that can be solved quickly, which is beneficial for a real-time application. However, the existing LQMPC approach does not preserve OD relations and thus may send traffic to wrong destinations. This problem is tackled by a heuristic method presented is this paper. We present two macroscopic models: 1) a non-linear route-specific model which keeps track of traffic dynamics for each OD pair and 2) a linear model that aggregates all route traffic states, which can be embedded into the LQMPC framework. The route-specific model predicts traffic dynamics and provides information to the LQMPC before the optimization and evaluates the optimal solutions after the optimization. The information obtained from the route-specific model is formulated as constraints in the LQMPC to narrow the solution space and exclude unrealistic solutions that would lead to flows that are inconsistent with the OD relations. The extended LQMPC approach is tested in a synthetic network with multiple bottlenecks. The simulation of the LQMPC approach achieves a total time spent close to the system optimum, and the computation time remains tractable.

AB - This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the LQMPC has a linear quadratic formulation that can be solved quickly, which is beneficial for a real-time application. However, the existing LQMPC approach does not preserve OD relations and thus may send traffic to wrong destinations. This problem is tackled by a heuristic method presented is this paper. We present two macroscopic models: 1) a non-linear route-specific model which keeps track of traffic dynamics for each OD pair and 2) a linear model that aggregates all route traffic states, which can be embedded into the LQMPC framework. The route-specific model predicts traffic dynamics and provides information to the LQMPC before the optimization and evaluates the optimal solutions after the optimization. The information obtained from the route-specific model is formulated as constraints in the LQMPC to narrow the solution space and exclude unrealistic solutions that would lead to flows that are inconsistent with the OD relations. The extended LQMPC approach is tested in a synthetic network with multiple bottlenecks. The simulation of the LQMPC approach achieves a total time spent close to the system optimum, and the computation time remains tractable.

KW - Computational modeling

KW - linear model

KW - Merging

KW - Model predictive control

KW - Optimization

KW - Predictive control

KW - Predictive models

KW - Roads

KW - route guidance

KW - Routing

KW - signal control

KW - spillback.

UR - http://www.scopus.com/inward/record.url?scp=85056330737&partnerID=8YFLogxK

U2 - 10.1109/TITS.2018.2877259

DO - 10.1109/TITS.2018.2877259

M3 - Article

VL - 20

SP - 3647

EP - 3660

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 10

ER -

ID: 29890007