Emma Frejinger

Adaptive Route Choice Models in Stochastic Time-Dependent Networks

Adaptive route choice models are studied that explicitly capture travelers’ route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next links out of the node. Existing route choice models that can be estimated on disaggregate revealed preferences assume a deterministic network setting from the travelers perspective and cannot capture the travelers proactive adaptive behavior under uncertain traffic conditions. The literature includes a number of algorithmic studies of optimal routing policy problems, but the estimation of a routing policy choice model is a new research area. The specifications of estimating the two adaptive route choice models are established and the feasibility of estimation from path observations is demonstrated on an illustrative network. Prediction results from three models–nonadaptive path model, adaptive path model, and routing policy model–are compared. The routing policy model is shown to better capture the option value of diversion than the adaptive path model. The difference between the two adaptive models and the nonadaptive model is larger in terms of expected travel time if the network is more stochastic, indicating that the benefit of adaptivity is more significant in a more unpredictable network.

On path generation algorithms for route choice models

Abstract This paper deals with choice set generation for the estimation of route choice models. Two different frameworks are presented in the literature: one aims at generating consideration sets and one samples alternatives from the set of all paths. Most algorithms are designed to generate consideration sets but fail in general to do so because some observed paths are not generated. In the sampling approach, the observed path as well as all considered paths is in the choice set by design. However, few algorithms can be actually used in the sampling context. In this paper, we present the two frameworks, with an emphasis on the sampling approach, and discuss the applicability of existing algorithms to each of the frameworks.

The load planning problem for double-stack intermodal trains

This paper presents a general methodology that addresses the load planning problem for intermodal trains. We propose a model that can deal with single- or double-stack railcars as well as arbitrary containers-to-cars matching rules. Moreover, we model weight and center of gravity constraints, stacking rules and technical loading restrictions associated with specific container types and/or contents. We propose an integer linear programming (ILP) formulation whose objective is to choose the optimal subset of containers and the optimal way of loading them on outbound railcars so as to minimize the resulting loading cost. An extensive numerical study is conducted. It shows that ignoring center of gravity constraints and containers-to-cars matching rules may lead to an overestimation of the train capacity and to select load plans that are not feasible in practice. We also show that we can solve realistic instances to optimality in reasonable computational time using a commercial ILP solver.