Towards Disaggregate Dynamic Travel Forecasting Models

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 performance, through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.     

Candidate lung tumor susceptibility genes identified through whole-genome association analyses in inbred mice

We performed a whole-genome association analysis of lung tumor susceptibility using dense SNP maps ( approximately 1 SNP per 20 kb) in inbred mice. We reproduced the pulmonary adenoma susceptibility 1 (Pas1) locus identified in previous linkage studies and further narrowed this quantitative trait locus (QTL) to a region of less than 0.5 Mb in which at least two genes, Kras2 (Kirsten rat sarcoma oncogene 2) and Casc1 (cancer susceptibility candidate 1; also known as Las1), are strong candidates. Casc1 knockout mouse tumor bioassays showed that Casc1-deficient mice were susceptible to chemical induction of lung tumors. We also found three more genetic loci for lung adenoma development. Analysis of one of these candidate loci identified a previously uncharacterized gene Lasc1, bearing a nonsynonymous substitution (D102E). We found that the Lasc1 Glu102 allele preferentially promotes lung tumor cell growth. Our findings demonstrate the prospects for using dense SNP maps in laboratory mice to refine previous QTL regions and identify genetic determinants of complex traits.