Automaattiset liikenteen ohjaus- ja tiedotusjärjestelmät : järjestelmien rakenne ja toiminta

320075

Methodological aspects of field operational tests of after-market and nomadic driver support systems and impacts on mobility

Background: This paper reports on the methodology undertaken and some results achieved within a study of drivers using aftermarket and nomadic devices (the TeleFOT project). Objective: To evaluate the methodology for conducting Field Operational Tests for Information and Communication Technology whilst also providing an example of the method applied in the context of mobility within the TeleFOT project. Method: ‘Top down, bottom up’ approach to the derivation of research questions and hypotheses is described. Statistical analysis has been undertaken on data collected through Field Operational Tests and Travel Diaries considering the impact of information functions (such as navigation, traffic information and green driving) upon journey length. Results: A summary of the results relating specifically to how the length of a journey can be affected by information functions indicates that Navigation and Traffic information can reduce the length of journeys whilst Green Driving functions tend to increase the journey length. Conclusion: The FOT methodology was successfully applied in the TeleFOT project as was the novel method for generating research questions. When turning the theoretical FOT method developed in FESTA into practice, several good innovations were made which and can be recommended for future FOTs; collation of metadata, the use of comparable origin / destination pairs for analysis, centralised processing of raw data into legs in order to simplify the analysis of the huge datasets collected in the project

Pääteiden lyhyen aikavälin matka-aikaennusteet

320073

Short-term prediction of traffic flow status for online driver information

The principal aim of this study was to develop a method for making a short-term prediction model of traffic flow status (i.e. travel time and a five-step travel-speed-based classification) and test its performance in the real world environment. Specifically, the objective was to find a method that can predict the traffic flow status on a satisfactory level, can be implemented without long delays and is practical for real-time use also in the long term. A sequence of studies shows the development process from offline models with perfect data to online models with field data. Models were based on MLP neural networks and self-organising maps. The purpose of the online model was to produce real-time information of the traffic flow status that can be given to drivers. The models were tested in practice. In conclusion, the results of online use of the prediction models in practice were promising and even a simple prediction model was shown to improve the accuracy of travel time information especially in congested conditions. The results also indicated that the self-adapting principle improved the performance of the model and made it possible to implement the model quite quickly. The model was practical for real-time use also in the long term in terms of the number of carry bits that it requires to restore the history of samples of traffic situations. As self-adapting this model performed better than as a static version i.e. without the self-adapting feature, as the proportion of correctly predicted traffic flow status increased considerably for the self-adapting model during the online trial