T abundance of highly disaggregate data (e.g., at five-second intervals) raises the question of the optimal data interval to estimate advertising carryover. The literature assumes that (1) the optimal data interval is the interpurchase time, (2) too disaggregate data causes a disaggregation bias, and (3) recovery of true parameters requires assumption of the underlying advertising process. In c...

Considerably less research has been done on modeling freight demand with disaggregate discrete models than on modeling passenger demand. The principal reason for this imbalance is the lack of freight demand data. Freight demand characteristics are expensive to obtain and are sometimes confidential. This paper analyzes the freight demand characteristics that drive modal choice by means of a larg...

The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro-simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptu...

The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models. The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network p...

It is not understood how Hsp104, a hexameric AAA+ ATPase from yeast, disaggregates diverse structures, including stress-induced aggregates, prions, and α-synuclein conformers connected to Parkinson disease. Here, we establish that Hsp104 hexamers adapt different mechanisms of intersubunit collaboration to disaggregate stress-induced aggregates versus amyloid. To resolve disordered aggregates, H...