Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks

This article presents two new models of Dynamic User Equilibrium that are particularly suited for ITS applications, where the evolution of vehicle flows and travel times must be simulated on large road networks, possibly in real-time. The key feature of the proposed models is the detail representation of the main congestion phenomena occurring at nodes of urban networks, such as vehicle queues and their spillback, as well as flow conflicts in mergins and diversions. Compared to the simple word of static assignment, where only the congestion along the arc is typically reproduced through a separable relation between vehicle flow and travel time, this type of DTA models are much more complex, as the above relation becomes non-separable, both in time and space. Traffic simulation is here attained through a macroscopic flow model, that extends the theory of kinematic waves to urban networks and non-linear fundamental diagrams: the General Link Transmission Model. The sub-models of the GLTM, namely the Node Intersection Model, the Forward Propagation Model of vehicles and the Backward Propagation Model of spaces, can be combined in two different ways to produce arc travel times starting from turn flows. The first approach is to consider short time intervals of a few seconds and process all nodes for each temporal layer in chronological order. The second approach allows to consider long time intervals of a few minutes and for each sub-model requires to process the whole temporal profile of involved variables. The two resulting DTA models are here analyzed and compared with the aim of identifying their possible use cases. A rigorous mathematical formulation is out of the scope of this paper, as well as a detailed explanation of the solution algorithm. The dynamic equilibrium is anyhow sought through a new method based on Gradient Projection, which is capable to solve both proposed models with any desired precision in a reasonable number of iterations. Its fast convergence is essential to show that the two proposed models for network congestion actually converge at equilibrium to nearly identical solutions in terms of arc flows and travel times, despite their two diametrical approaches wrt the dynamic nature of the problem, as shown in the numerical tests presented here

Task Assignment and Path Planning for Autonomous Mobile Robots in Stochastic Warehouse Systems

The material handling industry is in the middle of a transformation from manual operations to automation due to the rapid growth in e-commerce. Autonomous mobile robots (AMRs) are being widely implemented to replace manually operated forklifts in warehouse systems to fulfil large shipping demand, extend warehouse operating hours, and mitigate safety concerns. Two open questions in AMR management are task assignment and path planning. This dissertation addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in a warehouse environment. The goals are to maximize system productivity by avoiding AMR traffic and reducing travel time. The first topic in this dissertation is the development of a discrete event simulation modeling framework that can be used to evaluate alternative traffic control rules, task assignment methods, and path planning algorithms. The second topic, Risk Interval Path Planning (RIPP), is an algorithm designed to avoid conflicts among AMRs considering uncertainties in robot motion. The third topic is a deep reinforcement learning (DRL) model that is developed to solve task assignment and path planning problems, simultaneously. Experimental results demonstrate the effectiveness of these methods in stochastic warehouse systems

Coordinated Formation Control for Intelligent and Connected Vehicles in Multiple Traffic Scenarios

In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one-dimensional scenario, different formation geometries are analyzed and the interlaced structure is mathematically modelized to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles and target positions is solved using Hungarian Algorithm to improve the flexibility of the method in multiple scenarios. In the two-dimensional scenario, an improved virtual platoon method is proposed to transfer the complex two-dimensional passing problem to the one-dimensional formation control problem based on the idea of rotation projection. Besides, the vehicle regrouping method is proposed to connect the two scenarios. Simulation results prove that the proposed multi-vehicle formation control framework can apply to multiple typical scenarios and have better performance than existing methods

An investigation of TNAV equipped aircraft in a simulated en route metering environment

This document presents the results of an effort to estimate how often a TNAV (Time Navigation) equipped aircraft could be given a TNAV clearance in the En Route Metering (ERM) system as a function of the percentage of arriving traffic which is TNAV equipped. A fast-time simulation of Denver Stapleton international arrival traffic in the Denver Air Route Traffic Control Center route structure, including en route metering operations, was used to develop data on estimated conflicts, clearance communications and fuel usage for traffic mixes of 25, 50, 75 and 100% TNAV equipped. This study supports an overall effort by NASA to assess the benefits and required technology for using TNAV-equipped aircraft in the ERM environment

Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance

Design, protocol and routing algorithms for survivable all-optical networks.

by Hui Chi Chun Ronald.Thesis submitted in: December 1998.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 62-66).Abstract also in Chinese.Chapter Chapter 1. --- Introduction --- p.1Chapter Chapter 2. --- AON Architecture --- p.7Chapter 2.1 --- WCC Dimension Reduction Node Architecture --- p.10Chapter 2.2 --- Restoration of a Survivable AON --- p.13Chapter Chapter 3. --- Network Dimensioning Problem --- p.15Chapter 3.1 --- Problem Setting --- p.16Chapter 3.2 --- Two Solution Approaches --- p.16Chapter 3.2.1 --- Minimum Variance Algorithm (MVA) --- p.17Chapter 3.2.2 --- Minimum Variance Subroutine (MVS) --- p.19Chapter 3.3 --- Shortest Path Algorithm (SPA) --- p.21Chapter 3.4 --- An Illustrative Example --- p.22Chapter 3.5 --- Performance Comparisons --- p.26Chapter Chapter 4. --- Network Management for AON Restoration --- p.31Chapter 4.1 --- Surveillance Network --- p.31Chapter 4.2 --- Signaling Network --- p.32Chapter 4.3 --- Network Management System --- p.32Chapter 4.4 --- CCS7 Adaptation for Supporting AON Restoration --- p.34Chapter Chapter 5. --- Complete Restoration Algorithm for AON --- p.40Chapter 5.1 --- Link-Based Restoration Algorithm --- p.43Chapter 5.2 --- Source-Based Restoration Algorithm --- p.44Chapter 5.3 --- Case Studies --- p.45Chapter 5.3.1 --- Case I and II --- p.45Chapter 5.3.2 --- Case III --- p.50Chapter 5.4 --- Completely Restorable Network planning --- p.52Chapter 5.5 --- A Summary on Problem Formulations --- p.55Chapter Chapter 6. --- Conclusion --- p.57Reference --- p.6