Dynamic thresholds for the resilience assessment of road traffic networks to wildfires

The severe effects of extreme wildfire events in recent years have shown that the fire suppression approach is not enough to solve the problem. An alternative to dealing with this issue is to accept the impossibility of eliminating wildfire hazards and focus on preparing systems to be more resilient. However, existing decision-making tools...

Adaptive Parameterized Control for Coordinated traffic Management Using Reinforcement Learning

Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...

Robustness Analysis of Platoon Control for Mixed Types of Vehicles

Currently, with the development of driving technologies, driverless vehicles gradually are becoming more and more available. Therefore, there would be a long period of time during which self-driving vehicles and human-driven vehicles coexist. However, for a mixed platoon, it is hard to control the formation due to the existence of the manual...

Traffic network management: "Comparing algorithms for network-wide traffic management using Eclipse SUMO: A pragmatic approach versus Model Predictive Control"

The need for smart traffic control has grown over the last years. Initiated by an increased amount of traffic. Network-wide traffic control is becoming a more interesting field for traffic control. Mainly because computer power has increased and optimisation techniques improved. Network-wide traffic aims to improve the overall traffic state by...

Combined model predictive control and parking resource allocation in urban traffic networks

Urban areas with many daily commuters often experience a reduction in traffic flow during rush hours. In some urban areas where this reduction in traffic flow occurs, there are popular destinations for leisure that have multiple large parking facilities. Vehicles searching for vacant parking spots drive at a lower vehicle speed, reducing the...

Efficient evaluation of stochastic traffic flow models using Gaussian process approximation

This paper studies a Gaussian process approximation for a class of stochastic traffic flow models. It can be used to efficiently and accurately evaluate the joint (in the spatial and temporal sense) distribution of vehicle-density distributions in road traffic networks of arbitrary topology. The Gaussian approximation follows, via a scaling...

Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network

With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a...

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...

Understanding bikeability: a methodology to assess urban networks

A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple...

A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion...

Structured expert judgment to understand the intrinsic vulnerability of traffic networks

The concept of intrinsic vulnerability of a traffic network is defined for the first time in this paper. Intrinsic vulnerability is the susceptibility to incidents characterised by a probability of occurrence in space and time of difficult estimation, which can result in considerable reduction or loss of the system functionality. Given the...

A Hierarchical Control Framework for Coordination of Intersection Signal Timings in All Traffic Regimes

In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer--the network coordination layer--uses a model predictive control strategy based on a...

Stochastic Link Flow Model for signalized traffic networks with uncertainty in demand

In order to investigate the stochastic features in urban traffic dynamics, we propose a Stochastic Link Flow Model (SLFM) for signalized traffic networks with demand uncertainties. In the proposed model, the link traffic state is described using four different link state modes, and the probability for each link state mode is determined based...

Linear Robust Model Predictive Control for Urban Traffic Networks

In the last decades there has been a significant increase of traffic demand in urban areas due to the development of the economy and the increase of population. The increase in traffic demand leads to a more congested road network. Congestion causes unwanted delays, resulting in higher travel costs, noise, and pollution. Popular methods to...

Network-wide Traffic State Estimation using the Macroscopic Fundamental Diagram: A data fusion approach

Optimal and Robust Switching Control Strategies: Theory, and Applications in Traffic Management

Macroscopic modeling, predictive and robust control and route guidance for large-scale freeway and urban traffic networks are the main focus of this thesis. In order to increase the efficiency of our control strategies, we propose several mathematical and optimization techniques. Moreover, in the second part of the thesis, based on the hybrid...

Efficient model predictive control for large-scale urban traffic networks

Model Predictive Control is applied to control and coordinate large-scale urban traffic networks. However, due to the large scale or the nonlinear, non-convex nature of the on-line optimization problems solved, the MPC controllers become real-time infeasible in practice, even though the problem is solvable in theory. In this thesis, we mainly...