1,721,251 research outputs found
Short-term prediction of travel time using neural networks on an interurban highway
No full textThe main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times
Key performance indicators for assessing the impacts of automation in road transportation:Results of the Trilateral key performance indicator survey
No full textThis report documents the survey, which was designed to investigate views on the importanceof different key performance indicator (KPIs) for expressing the impact of automation in roadtransportation in several impact areas. It documents the rating results and additional KPIsproposed by the 77 experts in Europe, US and Japan who filled in the survey.The Trilateral Impact Assessment Subgroup of ART WG will use these results when decidingthe recommendations for the KPIs to be used in the impact assessment studies. Therecommendations and a full list of potential KPIs (KPI repository) will be added to the version2.0 of their impact assessment framework (expected in April 2018)
Liikennetilanneohjatut muuttuvat nopeusrajoitukset pääteiden liittymässä : vaikutusten arviointi ja järjestelmän kehittäminen : VIKING
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Moottoritieliikenteen mallintaminen - mikroskooppinen simulointi ja liikennevirran peruskuvaajat
Get PDFKehä III:n lyhyen aikavälin matka-aikaennustemalli : vanhaan seurantajärjestelmään perustuva malli
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Experience from a pilot trial
No full textThis study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. "br/"The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. "br/"However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. "br/"The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements
Short-term prediction of traffic flow status for online driver information
Get PDFThe 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
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