The uncertainty of an origin–destination (O–D) trip table estimate is affected by two factors: (i) the multiplicity of solutions due to the underspecified nature of the problem, and (ii) the errors of traffic counts. In this paper, a confidence interval estimation procedure for path flow...

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin-destination (O-D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic...

This paper proposes an alternate formulation for the combined distribution and assignment (CDA) problem, which seeks to determine consistent level-of-service and flow values of the trip distribution and traffic assignment steps. The CDA problem is modeled as a hierarchical travel choice problem...

In this paper, we extend the α-reliable mean-excess traffic equilibrium (METE) model of Chen and Zhou (Transportation Research Part B 44(4), 2010, 493–513) by explicitly modeling the stochastic perception errors within the travelers’ route choice decision processes. In the METE model, each...

In this paper, we propose a new model called the [alpha]-reliable mean-excess traffic equilibrium (METE) model that explicitly considers both reliability and unreliability aspects of travel time variability in the route choice decision process. In contrast to the travel time budget (TTB) models...

This paper presents a path-based traffic assignment formulation and its solution algorithm for solving an asymmetric traffic assignment problem based on the TRANSYT traffic model, a well-known procedure to determine the queues and delays in a signal-controlled network with explicit...

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we...

In this paper, a predictive dynamic traffic assignment model in congested capacity-constrained road networks is formulated. A traffic simulator is developed to incrementally load the traffic demand onto the network, and updates the traffic conditions dynamically. A time-dependent shortest path...

In this paper we propose a model and algorithm for solving the equilibrium assignment problem in a congested, dynamic and schedule-based transit network. We assume that the time varying origin-destination trip demand is given. All travelers have full predictive information (that have been gained...