Mixed-integer programming in motion planning
This paper presents a review of past and present results and approaches in the area of
motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP …
motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP …
Traffic signal timing via deep reinforcement learning
In this paper, we propose a set of algorithms to design signal timing plans via deep
reinforcement learning. The core idea of this approach is to set up a deep neural network …
reinforcement learning. The core idea of this approach is to set up a deep neural network …
[HTML][HTML] A consensus-based distributed trajectory control in a signal-free intersection
This paper develops a distributed cooperative control logic to determine conflict-free
trajectories for connected and automated vehicles (CAVs) in signal-free intersections. The …
trajectories for connected and automated vehicles (CAVs) in signal-free intersections. The …
Large-scale traffic signal control using a novel multiagent reinforcement learning
Finding the optimal signal timing strategy is a difficult task for the problem of large-scale
traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method …
traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method …
[HTML][HTML] A real-time network-level traffic signal control methodology with partial connected vehicle information
This paper presents two algorithms to estimate traffic state in urban street networks with a
mixed traffic stream of connected and unconnected vehicles and incorporates them in a real …
mixed traffic stream of connected and unconnected vehicles and incorporates them in a real …
A macroscopic model with the advantages of microscopic model: A review of Cell Transmission Model's extensions for urban traffic networks
L Adacher, M Tiriolo - Simulation Modelling Practice and Theory, 2018 - Elsevier
This paper reports a review of the extensions and application of the Cell Transmission
Model (CTM). Those extensions are models able to simulate complex urban traffic dynamics …
Model (CTM). Those extensions are models able to simulate complex urban traffic dynamics …
Hierarchical control for stochastic network traffic with reinforcement learning
This study proposes a hierarchical control framework to maximize the throughput of a road
network driven by travel demand with uncertainties. In the upper level, a perimeter controller …
network driven by travel demand with uncertainties. In the upper level, a perimeter controller …
Asynchronous n-step Q-learning adaptive traffic signal control
Ensuring transportation systems are efficient is a priority for modern society. Intersection
traffic signal control can be modeled as a sequential decision-making problem. To learn how …
traffic signal control can be modeled as a sequential decision-making problem. To learn how …
Corridor level cooperative trajectory optimization with connected and automated vehicles
Trajectory planning for connected and automated vehicles (CAVs) has been studied at both
isolated intersections and multiple intersections under the fully CAV environment in the …
isolated intersections and multiple intersections under the fully CAV environment in the …
An optimization model and traffic light control scheme for heterogeneous traffic systems
In this paper, one type of heterogeneous traffic network, which consists of signalized
intersections and non-signalized ones, is analyzed and modelled with a macroscopic model …
intersections and non-signalized ones, is analyzed and modelled with a macroscopic model …