Formulation and Estimation of Combined Network Equilibrium Models with Applications to Stockholm

Abstract

Combined network equilibrium models of trip distribution, modal split, and route choice are formulated and estimated for the Stockholm region. These models take into account the feedback effects among three stages of the classical four-step model. A simultaneous structure of the mode and destination choices is studied together with nested combined models reflecting conditional choice probabilities. In the traditional combined model, mode choice is assumed conditional on destination choice. The reverse combined model, in contrast, considers destination choice conditional on mode choice. User-optimal traffic assignment is integrated with these approaches to modeling travel demand in network equilibrium models. The three combined models are estimated using the full-information maximum likelihood technique implying that all preference parameters are estimated simultaneously. The models are estimated on medium-size travel and network data for the Stockholm region in 1986. The results suggest a rejection of the traditional nested model because of incorrect relative values of the estimated cost-sensitivity parameters. The reverse nested model is preferred, even if the overall goodness-of-fit is better for the traditional nested model. An application to a future scenario for the year 2020 is included.

Stochastic equilibrium assignment with variable demand: literature review, comparisons and research needs

“…Fernandez et al [47] deal with the assignment problem with combined transport modes; Nagurney et al [48] propose an optimization model oriented to evaluate mitigation strategies of pollutants emission, the solution algorithm is based on the modified projection method introduced by Korpelevich [49]. Abrahamsson and Lundqvist [50] take into account destination choice and mode choice, and assume that transport modes do not interact with each other. They propose an optimization model solved through the partial linearization technique implemented by Boyce et al [51].…”

Calibration and validation of parking search-time function

“…Lam et al (2006) formulated an equilibrium model which solves user equilibrium flows using a time dependent approach with multiple user classes and multiple parking facilities connected to destinations by walk links. It is assumed that the drivers initially make a joint choice of departure time and parking duration, before deciding on a route that minimises the overall disutility from an origin to a destination, thus replicating a hierarchical choice structure, much in a similar manner to that in Abrahamsson and Lundqvist (1999), for example. The main finding of this study is that the parking behaviour of drivers is significantly influenced by factors such as level of demand, capacity of car parks, parking charges and the distance of car park to the ultimate destination.…”

A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model

“…Other important applications of maximum entropy are the combined models which integrate the different transportation decisions including trip generation, destination choice, mode choice and route choice. These models first appeared in the early 1980s and were followed over the next two decades by further important developments (e.g., [1,[11][12][13][14][15][16][17][18][19][20][21]). In every maximum entropy application reported in the specialized literature, the demand model is the solution of an entropy maximization problem (or an equivalent formulation) with exogenous parameters in its objective function and a set of linear constraints.…”