This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors reach consensus of common variables. A receive...

Network planning and traffic flow optimization requires the acquirement and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements etc. Data acquirement is an expensive process that involves household surveys and automatic as well as semi-automatic measurements performed all over the network. For example, in ord...

1.1 INTRODUCTION Traditional models of traac assignment usually assume that ow is distributed along paths of the underlying network. In this presentation, we argue that several models can be enhanced by considering that the physical path travelled by a given user depends on a sequence of events (random or not) that occur at the nodes of the network, and whose outcome is only revealed once the u...

We study the problem of computing traffic assignments for public transit networks: Given a public transit network and a demand (i.e. a list of passengers, each with associated origin, destination, and departure time), the objective is to compute the utilization of every vehicle. Efficient assignment algorithms are a core component of many urban traffic planning tools. In this work, we present a...

A cost driven traffic assignment model is proposed to maximise the global benefit of transportation network users. A decentralised primal-dual time synchronised approach is used to solve for the optimal traffic rates subjecting the objective function to the bounds of the network tolerable costs. For a successful operation of the transportation network, the affordable upper bounds on the costs h...

We consider the issue of task assignment in a distributed system under heavy-tailed (ie. highly variable) workloads. A new adaptable approach called TAPTF (Task Assignment based on Prioritising Traffic Flows) is proposed, which improves performance under heavy-tailed workloads for certain classes of traffic. TAPTF controls the influx of tasks to each host, enables service differentiation throug...