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The temporal transferability of mode choice and trip distribution models was studied by using the data based on traffic surveys in the Helsinki, Finland, metropolitan area in 1981 and 1988. The updating procedures examined were the Bayesian updating, combined transfer estimation, transfer scaling, and joint context estimation procedures. The results of model updating indicated that finding the correct method and sample size for each case is not an unambiguous task. The best method depends on the difference in model coefficients between the initial and the final stages as well as the quality of the data. According to the statistical tests, no differences could be discerned between the models at all. However, the sample enumeration test proved that the models’ ability to predict changes in behavior can vary greatly according to the method used. On the basis of this research the transfer scaling seems to be the method best suited for simple models. In particular, the method is quite useful if the transfer bias is large. The combined transfer estimation procedure performs best when there is a great number of observations and the transfer bias is small. With small sample sizes the Bayesian approach and the joint context estimation give the best results.

Promoting pedestrian and bicycle traffic in the Põlva region is an exemplary project in Estonia. The project is financed by the EU. A report of the initial stages of the project was completed at the end of 2005. The main planning target is a region-wide and multifunctional walking and cycling network. The project work was started in 2005. As the EU project in Põlva is intended to serve as a model for other towns and municipalities in Estonia, a manual titled "Planning pedestrian and bicycle traffic network in Municipalities" was issued, based on Finland's expertise and experience. No other similar manuals to our knowledge have been issued in Europe. The Põlva project is also a matching example of Finnish-Estonian cooperation.

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A long term road safety programme (called LINTU), funded by the Ministry of Transportation and Communications, Finland, Finnish Road Administration and Finnish Vehicle Administration, was launched in 2002. This research is one of the several independent subprojects of the LINTU-programme. The paper presents a tool for analyzing the state of traffic safety in Finnish municipalities. In addition, variables having the most significant effect on differences between municipalities from traffic safety viewpoint were examined by means of cluster analysis. Results of the cluster analysis proved that it is possible to profile and categorize the municipalities according to the frequency of different accident types and by the degree of urbanization. It is difficult to find any other background variables that explain accidents in municipalities largely because accidents usually happen as a sum of many incidents and human errors. Accordingly, even if some explanatory variables could be found, it would still not be obvious that accidents in different municipalities were caused by the same reasons. It was shown as well that accidents of certain types do not follow any distribution by area. However, it is possible to find indicators that enable the status of traffic safety in municipalities of similar types to be foreseen. The safest municipalities were urban-type municipalities with rather compact land use, whereas the lowest safety level was in rural areas with a high proportion of through traffic.