Novel road classifications for large scale traffic networks

Toplak, Werner; Koller, Hannes; Dragaschnig, Melitta; Bauer, Dietmar; Asamer, Johannes

doi:10.1109/itsc.2010.5625182

Cited by 14 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset represents one-year worth of FCD collected on workdays (Monday to Friday) by about 3500 taxis (for details, see Leodolter et al, 2015) which were preprocessed using the FCD software FLEET (Fleet Logistics Service Enhancement with EGNOS and Galileo Satellite) (Toplak, Koller, Dragaschnig, Bauer, & Asamer, 2010). This includes the following steps: first, the raw GPS measurements are projected onto the street network graph using map matching techniques described in Koller, Widhalm, Dragaschnig, and Graser (2015).…”

Section: Resultsmentioning

confidence: 99%

Improving vehicle speed estimates using street network centrality

Gräser

Leodolter

Koller

et al. 2016

International Journal of Cartography

Self Cite

View full text Add to dashboard Cite

This paper describes a novel approach to improve prediction models which estimate vehicle speeds and their diurnal variation for road network links in urban street networks using only static map attributes. The presented approach takes into account previously neglected spatial information by integrating network centrality measures for closeness (indicating how central a link is) and betweenness (indicating how important a road link is) into the prediction model. The model is calibrated with a real-world dataset of 100 million individual speed measurements from a fleet of 3500 taxi probe vehicles in Vienna, Austria. Given that centrality can be derived directly from readily available street network data, the experimental results demonstrate that integrating centrality measures considerably improves the predictions without the need for adding a supplementary data source. Improvements for vehicle speed estimates are particularly prevalent on important street network links in the city center as well as rural streets in the periphery. RÉSUMÉCe papier décrit une nouvelle approche pour améliorer un modèle prédictif dont le but est d'estimer les vitesses des véhicules et leurs variations diurnes sur un réseau routier en relation avec un réseau de rues, en n'utilisant que des attributs cartographiques statiques. L'approche présentée prend en compte une information spatiale négligée précédemment en intégrant dans le modèle prédictif deux mesures de centralité : la centralité de proximité 'closeness' (qui estime la centralité d'un arc) et la centralité intermédiaire 'betweeness' (qui estime l'importance d'un arc routier). Le modèle est calibré avec des données réelles à partir d'une base de 100 millions d'enregistrements de vitesses correspondant à une flotte de 3500 taxi à Vienne en Autriche. La centralité est directement dérivée des données de réseau de rues disponibles. Les résultats expérimentaux montrent que l'intégration des mesures de centralité améliore considérablement les prédictions sans avoir à ajouter des informations supplémentaires. Les améliorations de l'estimation de la vitesse des véhicules sont particulièrement significatives sur le réseau des rues importantes en centre-ville ainsi que sur les routes du réseau rural en périphérie. ARTICLE HISTORY

show abstract

Section: Resultsmentioning

confidence: 99%

Improving vehicle speed estimates using street network centrality

Gräser

Leodolter

Koller

et al. 2016

International Journal of Cartography

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various methods have been proposed to overcome this problem, mainly focused on using the information in time and space dimension. For time dimension, Chrobok et al employed historical data with similar traffic behavior to aggregate missing data, which is based on road classification schemes such as Tele Atlas Functional Road Classes [4,17] and day categories [18]. Without pre-classification, Widhalm et al [19] presented a GMM based method to learn information about typical shapes of the diurnal speed time series.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…If all linked section is uninstalled sensors (n ¼0), the RTS is estimated by HAM. In SCM, the missing data is estimated by n sections with the highest correlations [18], where the upper limit of n is set at 6. Finally, the root mean square error (RMSE) is applied to evaluate the estimate error [54], which is defined as…”

Section: Traffic Speed Estimationmentioning

confidence: 99%

Camera location for real-time traffic state estimation in urban road network using big GPS data

Shan

Zhu

2015

Neurocomputing

View full text Add to dashboard Cite

“…This leaves floating vehicles as the main source of information for this approach. Early examples in this respect are taxi floating car measurements established as early as 2004 in Vienna [9]. Hereby taxis are equipped with GPS sensors that record the location of the taxi typically with a small sampling rate of approximately one minute due to technical restrictions 1 .…”

Section: Introductionmentioning

confidence: 99%

Travel time predictions: should one model speeds or travel times?

Bauer

Tulic

2018

Eur. Transp. Res. Rev.

Self Cite

View full text Add to dashboard Cite

Purpose: Travel time predictions are of importance for individual trip planning as well as for logistics applications. Since travel time and travel speed have a one-one correspondence, the modeller has the choice to model travel times directly or model the corresponding travel speeds and infer the associated time from the speed predictions. A priori it is not clear which of these is the superior approach. In this paper we investigate the implications of the choice of the methodology for the accuracy of the travel time predictions. Methods: For a selection of links, travel time prediction models, both in a direct way as well as indirectly via the implied link travel speeds, are obtained. The respective predictions are compared on a validation data set with respect to their accuracy as measured by mean error, root mean square error, mean percentage error as well as mean absolute percentage error. Additionally, the accuracy of route travel time predictions is evaluated based on the raw GPS data from the floating taxis. Results: The empirical results overwhelmingly make the case for using direct modelling if the goal of prediction is to obtain a RMSE-optimal prediction. If the MAPE is to be minimized, however, the indirect method provides the better results. Conclusion: Thus the goal of the prediction determines the better method of modelling: if one is interested in minimizing the RMSE, then, for the data investigated in this paper, the direct method should be selected. However, if one is interested in obtaining a small MAPE, the indirect method achieves better results.

show abstract

Novel road classifications for large scale traffic networks

Cited by 14 publications

References 3 publications

Improving vehicle speed estimates using street network centrality

Improving vehicle speed estimates using street network centrality

Camera location for real-time traffic state estimation in urban road network using big GPS data

Travel time predictions: should one model speeds or travel times?

Contact Info

Product

Resources

About