Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories

Koller, Hannes; Widhalm, Peter; Dragaschnig, Melitta; Gräser, Axel

doi:10.1109/itsc.2015.411

Cited by 30 publications

(21 citation statements)

References 3 publications

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“…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). Then, vehicle speed measurements are obtained by analyzing the routes between consecutive projected positions.…”

Section: Resultsmentioning

confidence: 99%

Improving vehicle speed estimates using street network centrality

Gräser

Leodolter

Koller

et al. 2016

International Journal of Cartography

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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

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Section: Resultsmentioning

confidence: 99%

Improving vehicle speed estimates using street network centrality

Gräser

Leodolter

Koller

et al. 2016

International Journal of Cartography

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“…Since their research, many studies have improved on this method. Koller et al [25] propose fast map matching (FMM) based on HMM which replaces the Viterbi algorithm with a bidirectional Dijkstra and employs a lazy evaluation to reduce the number of costly route calculations. Yang et al [26] also present a fast map matching, an algorithm integrating hidden Markov model with precomputation.…”

Section: Global Matching Methodsmentioning

confidence: 99%

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

Liu

Ling

et al. 2020

IJGI

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As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy.

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“…focus of several papers (e.g. [107], [108], [84] and [109]). A discussion of such works is provided in Chapter 5, which deals with this issue in detail.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…In the case of highly noisy location measurements, the great-circle distance between the observed locations may cause the measure of circuitousness to be significantly inaccurate. The second drawback relates to the fact that the transition probabilities computed based on the above measure may vary greatly for equally plausible transition paths depending on the sampling interval [88,108].…”

Section: Transition Probability Modelmentioning

confidence: 99%

“…However, they do not guarantee optimality and in some cases, produce erroneous outputs. An optimization that reduces the number of transition probability calculations (and thus the number of SSP computations) without compromising optimality has been proposed in [108]. It involves reformulating the problem of finding the most likely state sequence in a HMM trellis graph as a one-to-one shortest path problem and finding the state sequence without having to explore all the states in the trellis graph.…”

Section: Related Workmentioning

confidence: 99%

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Map matching imprecise trajectories for smart mobility applications

Jagadeesh¹

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Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories

Cited by 30 publications

References 3 publications

Improving vehicle speed estimates using street network centrality

Improving vehicle speed estimates using street network centrality

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

Map matching imprecise trajectories for smart mobility applications

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