text: 357,216,1375311361316634624,2647128003,Mogens Fosgerau,"A perturbed utility route choice model We propose a model in which a utility maximizing traveler assigns flow across an entire network under a flow conservation constraint. 1/ <LINK> #EconTwitter 2/ Substitution between routes depends on how much they overlap. This model can be estimated from route choice data, where the full set of route alternatives is included and no choice set generation is required. 3/3 Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization and is straightforward even for large networks. @JRehbeck @ERC_Research @cykelnorden @ThomasKjrRasmu2",https://arxiv.org/abs/2103.13784,"We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint}. Substitution between routes depends on how much they overlap. {\tr The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network. ",A perturbed utility route choice model,6,"['A perturbed utility route choice model\n\nWe  propose  a  model  in  which  a  utility  maximizing  traveler  assigns  flow  across  an  entire network under a flow conservation constraint.  1/ \n<LINK> #EconTwitter', '2/ Substitution between routes depends on how much they overlap. This model can be estimated from route choice data, where the full set of route alternatives is included and no choice set generation is required.', '3/3 Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization and is straightforward even for large networks.', '@JRehbeck', '@ERC_Research', '@cykelnorden @ThomasKjrRasmu2']",21,03,657