| 1. |
- Bierlaire, Michel, et al.
(författare)
-
A latent route choice model in Switzerland
- 2006
-
Ingår i: Proceedings of the European Transport Conference.
-
Konferensbidrag (refereegranskat)abstract
- Rature, for example the C-Logit (Cascetta et al., 1996), Path Size Logit (Ben-Akiva and Bierlaire, 1999), Link-Nested Logit (Vovsha and Bekhor, 1998) and subnetwork (Frejinger and Bierlaire, 2006) models. A latent cho- sen route corresponds to an unobserved choice where only an approximate choice description is available. Instead of an exact route description, trav- ellers describe their choice in terms of a sequence of locations and cities that they have traversed, without the need to relate the actual network used by the analyst. We compute the probability of an (aggregate) observation with an un- derlying route choice model using a detailed network description and actual paths. In this context, not only several routes can correspond to the same observation, but the exact origin-destination pair is not necessarily known. We therefore consider several possible origin-destination pairs and their as- sociated set of routes, generated by a choice set generation algorithm. We derive from this list the probability of each observation, in order to perform the maximum likelihood estimation of the route choice model. The methodology is illustrated by estimating Path Size Logit and sub- network models using a dataset collected in Switzerland. This application is one of few based on revealed preferences (RP) data that are presented in the literature. In addition, the network used here (39411 unidirectional links and 14841 nodes) is to our knowledge the largest network used for evaluation of route choice models based on RP data. The estimation results are very sat- isfactory. Indeed, they do not only show that it is possible to estimate route choice models based on aggregate observations, but also that the parameter estimates are stable across different types of models and that the standard errors are small.
|
|
| 2. |
|
|
| 3. |
- Fosgerau, Mogens, et al.
(författare)
-
A dynamic discrete choice approach for consistent route choice model estimation
- 2011
-
Ingår i: Proceedings of the Swiss Transport Research Conference.
-
Konferensbidrag (refereegranskat)abstract
- We propose a dynamic discrete choice approach for consistently estimating route choice model parameters based on path observations using maximum likelihood. The approach is computationally efficient and does not require choice set sampling.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Fosgerau, Mogens, et al.
(författare)
-
Route choice modeling without route choice
- 2009
-
Ingår i: Proceedings of the European Transport Conference.
-
Konferensbidrag (refereegranskat)abstract
- Route choice modeling is complex. The number of alternative paths is often very large, while the paths are likely to share unobserved attributes which induces correlation. When modelling this, we face a trade-off between using models that are simple enough to handle many alternative paths while at the same time being able to handle correlation. There is a substantial ongoing research effort seeking to resolve this dilemma, so far with limited success. For these reasons the multinomial logit model (path size logit and c-logit proposed by Ben-Akiva and Bierliare, 1999, and Cascetta et. al., 1996, respectively) is widely used in spite of its known limitations.The main purpose of this paper is to present and test a dynamic discrete choice approach for the estimation of the parameters of a route choice model. In the dynamic modeling approach, the individual is seen as taking sequential decisions on which link to choose, and the choices are made at the nodes in the network. The obvious advantage with this approach is that the choice set at every stage is quite small and well defined, while a correlation structure is naturally imposed among different paths, even if each sequential decision follows a multinomial logit model. From an econometric point of view, the link choice model can be a lot simper to deal with.The utility maximising choice of path may be broken down into a sequence of link choices, where at each stage the individual considers the utility associated with downstream link choices accumulated into a value function. However, if we were to compute the value function associated with the available link choices at every stage, the complexity of the problem would be at least the same as the original path choice problem. An exact solution method to calculate the value function runs into the curse of dimensionality when solving a dynamic programming problem. Therefore, the computational burden may be prohibitive for large networks if one tries to solve the dynamic programming problem by brute force. This is probably why the sequential approach is not used for estimating route choice models in spite of having been around for many years (e.g., Dial, 1971).However, it is not strictly necessary to solve the dynamic programming problem in order to estimate the parameters of the route choice model consistently. It is sufficient to find a suitable approximation to the value function. So the objective of this paper is to test whether it is possible to generate good predictors for the value function such that the parameters of the route choice model may be estimated on link choices rather than path choices. If this turns out to be possible, then both the econometric and computational complexity of route choice modelling may be dramatically reduced.The paper therefore discusses the conditions under which the route choice model can be consistently estimated. We then test the approach using simulated data for a real network (Borlänge, Sweden), where route choice observations are generated using the exact model, i.e. solving the dynamic programming problem. This allows us to compare the exact value functions with the approximations. We show how the approximation can be defined using proxy variables such as direction and distance to destination. The paper concludes with a discussion on the use of the model for prediction (policy analysis) and related issues.
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
- Frejinger, Emma, 1979-, et al.
(författare)
-
Capturing correlation in route choice models using subnetworks
- 2006
-
Ingår i: Proceedings of the Swiss Transportation Research Conference.
-
Konferensbidrag (refereegranskat)abstract
- When using random utility models for a route choice problem, choice set generation and correlation among alternatives are two issues that make the modelling complex. In this paper we propose a modelling approach where the path overlap is captured with a subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviourally relevant roads. In practise, the subnetwork can easily be defined based on the route network hierarchy. We propose a model where the subnetwork is used for defining the correlation structure of the choice model. The motivation is to explicitly capture the most important correlation without considerably increasing the model complexity. We present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured both by a Path Size attribute and error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlänge. The results show a significant increase in model fit for the Error Component model compared to a Path Size Logit and Multinomial Logit models. Moreover, the correlation parameters are significant. We also analyse the performance of the different models regarding prediction of choice probabilities. The results show a better performance of the Error Component model compared to the Path Size Logit and Multinomial Logit models.
|
|
