Iterative Learning Control Approach for Signaling Split in Urban Traffic Networks with Macroscopic Fundamental Diagrams

Fei Yan,Fu-Li Tian,Zhong-Ke Shi

doi:10.1155/2015/975328

Fei Yan, Fu-Li Tian + Show 1 more

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https://doi.org/10.1155/2015/975328

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Abstract

Recent analysis of field experiments in cities revealed that a macroscopic fundamental diagram (MFD) relating network outflow and network vehicle accumulation exists in the urban traffic networks. It has been further confirmed that an MFD is well defined if the network has regular network topology and homogeneous spatial distribution of vehicle accumulation. However, many real urban networks have different levels of heterogeneity in the spatial distribution of vehicle accumulation. In order to improve the mobility in heterogeneously congested networks, we propose an iterative learning control approach for signaling split, which aims at distributing the accumulation in the networks as homogeneously as possible and ensuring the networks have a larger outflow. The asymptotic convergence of the proposed approach is proved by rigorous analysis and the effectiveness is further demonstrated by extensive simulations.

Highlights

With the rapid development of urbanization and increasing demand on automobile, the elimination of traffic congestion or the reduction of traffic delays in urban areas becomes a challenging task for both traffic practitioners and researchers
In order to improve the mobility in heterogeneously congested networks, we propose an iterative learning control approach for signaling split, which aims at distributing the accumulation in the networks as homogeneously as possible and ensuring the networks have a larger outflow
From the macroscopic fundamental diagram (MFD) resulting for all the demand scenarios under the iterative learning control (ILC) strategy, it can be seen that the traffic states of the network are unsaturated or partly saturated, which means that the vehicles run more efficiently and smoothly in the network under the ILC strategy

Summary

Introduction

With the rapid development of urbanization and increasing demand on automobile, the elimination of traffic congestion or the reduction of traffic delays in urban areas becomes a challenging task for both traffic practitioners and researchers. Mathematical Problems in Engineering of this model-based approach, the macroscopic fundamental diagram (MFD) aiming at simplifying the modeling problem of traffic flow describes the evolution of network outflow and vehicle accumulation in the urban network at an aggregate level. We should take full advantage of the inherent repeatability of urban traffic flow to improve mobility and decrease delays in urban traffic networks To this end, we will apply the iterative learning control (ILC) method to solve the signal splitting for urban networks. The paper focuses on the development of the ILC controller based on MFD and its application to the control of signaling split in urban networks.

Macroscopic Fundamental Diagram of Urban Traffic Networks

Urban Traffic Network Modeling and Problem Formulation

The Investigated Control Strategy

Simulation Studies

Conclusion