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on Discrete Choice Models |
| By: | Richard A Iles; John M Rose |
| Keywords: | Stated Choice, experimental design, choice task simplification, cognitive burden, Indian primary health care, Attribute nonattendance |
| JEL: | I11 C93 |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:gri:epaper:economics:201305&r=dcm |
| By: | Fosgerau, Mogens (DTU TRANSPORT); Frejinger, Emma (KTH); Karlström, Anders (KTH) |
| Abstract: | This paper considers the path choice problem, formulating and discussing an econometric random utility model for the choice of path in a network with no restriction on the choice set. Starting from a dynamic specification of link choices we show that it is equivalent to a static model of the multinomial logit form but with infinitely many alternatives. The model can be consistently estimated and used for prediction in a computationally efficient way. Similarly to the path size logit model, we propose an attribute called link size that corrects utilities of overlapping paths but that is link additive. The model is applied to data recording path choices in a network with more than 3,000 nodes and 7,000 links. |
| Keywords: | Discrete choice; Recursive logit; Networks; Route choice; Infinite choice set |
| JEL: | R40 |
| Date: | 2013–09–16 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:ctswps:2013_010&r=dcm |
| By: | Fadaei Oshyani, Masoud (KTH); Sundberg, Marcus (KTH); Karlström, Anders (KTH) |
| Abstract: | GPS and nomad devices are increasingly used to provide data from individuals in urban traffic networks. In many different applications, it is important to predict the continuation of an observed path, and also, given sparse data, predict where the individual (or vehicle) has been. Estimating the perceived cost functions is a difficult statistical estimation problem, for different reasons. First, the choice set is typically very large. Second, it may be important to take into account the correlation between the (generalized) costs of different routes, and thus allow for realistic substitution patterns. Third, due to technical or privacy considerations, the data may be temporally and spatially sparse, with only partially observed paths. Finally, the position of vehicles may have measurement errors. We address all these problems using a indirect inference approach. We demonstrate the feasibility of the proposed estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered. |
| Keywords: | GPS; Route choice model; Indirect inference; Sparse data; Statistical estimation problem. |
| JEL: | R40 |
| Date: | 2013–09–16 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:ctswps:2013_011&r=dcm |