stochastic dynamic assignment model

In this work, we present a stochastic dynamic traffic assignment model for emergency evacuations that considers background traffic. First, the equilibrium functions of the entry time and path choice are formulated based on the logit model.

This paper proposes a stochastic dynamic traffic assignment (SDTA) model based user optimum considering the loss of node capacity and change of network structure under traffic and environment emergencies. The Nested Logit model is used to describe the departure time and path choice.

This research aims to develop a theoretical model for the stochastic dynamic traffic assignment for combined route and departure time choice in the networks with many-to-many origin destination flows.

A hybrid model is presented that handles the detailed assignment of drivers to loads, as well as handling forecasts of future loads. Numerical experiments demonstrate that our stochastic, dynamic model outperforms standard myopic models that are widely used in practice.

We develop a Markov decision process (MDP) model of a stochastic dynamic assignment problem, named the Air Battle Management Problem (ABMP), wherein a set of unmanned combat aerial vehicles (UCAV) must defend an asset from cruise missiles arriving stochastically over time.

The two main components of the dynamic traffic assignment models, namely, route choice and network loading, aim to capture vehicles’ movements dynamics in a network. Network loading mainly replicates vehicles’ movements in the network on different paths specified by the route choice model.

We present a solution method for discrete time periods, computational results on an illustrative network, including sensitivity analyses of the parameters, and comparisons with a previous suitable stochastic DTA model from the literature.

This paper is concerned with the system optimum-dynamic traffic assignment (SO-DTA) problem when the time-dependent demands are random variables with known probability distributions. The model is a stochastic extension of a deterministic linear programming formulation for SO-DTA introduced by Ziliaskopoulos (Ziliaskopoulos, A.K., 2000.

2. We review alternative models for the dynamic assignment problem, and discuss their strengths and weaknesses. 3. We develop a stochastic model of the dynamic booking process for truckload motor carriers, and show how it can be integrated into a stochastic network model for planning vehicle movements. 4. We develop a methodology for evaluating and

The proposed problem is formulated as a fixed point problem, which includes a Monte–Carlo-based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation.