Modelling transport and real-estate values interactions in urban systems

This article presents hedonic Multiple Linear Regression models (MLR), Spatial Auto-Regressive hedonic models (SAR), Spatial autoregressive hedonic in the Error term Models (SEM) and spatial Durbin hedonic Models (SDM) to estimate houses price variations in metropolitan areas as a result of changing environmental and accessibility conditions. The goodness of fit of the different models has been compared along with a series of hypotheses about the performance of the specifications considering spatial relationships between observations. The case study for such analysis was the urban area of Santander (Spain). It has been observed the models which considered spatial dependence between observations offered a greater degree of fit in a scenario showing strong spatial correlation in MLR residuals. The SEM model combined with a Queen-Contiguity matrix provided a good fit to the data and at the same time presented significant parameters with theoretically coherent signs. This model estimated increases of 1.8% for each additional transit line present in the areas of housing, as well as a reduction of 1.1% in their prices for each additional minute in travelling time to the Central Business District. Closeness to the train stations, however, implied reductions in house prices

Modelling the spatial interactions between workplace and residential location

The use of Multinomial Logit (MNL) models specification for the simulation of residential location have been often criticised due to the Independence of Irrelevant Alternatives hypothesis (IIA) which does not allow for the existence of spatial correlation between residential zones. Moreover, it is not clear when and to what extent the influence of the workplace zone and accessibility to employment affect the residential location choices made by households; in other word, whether the residing choice is conditional to the workplace, or vice versa; or if such choices (residence and work place) are joint. In this paper, Nested Logit (NL) and Cross-Nested Logit models of residential location choice are specified and compared to MNL, to investigate the existence of spatial correlation between different locations. Furthermore, different assumptions are tested, considering the choice of residential zone and the joint choice of residential zone and work place zone. The models were estimated for the urban area of Santander (Spain). The results indicate that the inclusion in the model specification, of the spatial correlation between zones fit the data significantly better. Home-work journey times were a statistically significant factor in household location choice, whereas accessibility to employment had the correct sign but it was not statistically significant

Analytic Comparison of Some Epidemic Models with Vaccination

AbstractIn this paper, we discuss the elementary properties of some simple SI, SR, SIR and SEIR epidemic models whose parameterizing functions (such as per-capita death rate, disease transmission, removal rate etc.) might be eventually time-varying but nonnecessarily time-integrable. Vaccination rules based of feedback, measuring the numbers of some of the partial populations defining the disease progress, are also discussed

Paying for parking : improving stated-preference surveys

This article describes an experiment which introduced random ranges into the variables used for the design of a stated preference survey and its effects on willingness to pay for parking. User behaviour at the time of parking was modelled to determine their willingness to pay in order to get to their final destination more quickly. Calculating willingness to pay is fundamental during the social and economic assessment of projects. It is important to correctly model how car parks and their users interact in order to get values which represent reality as closely as possible. Willingness to pay is calculated using a stated preference survey and by calibrating multinomial logit models, taking variable tastes into account. It is shown that a value with a low variability can be obtained for willingness to pay by correctly establishing the context of the choice and randomly changing the variables around an average value