Fast map matching, an algorithm integrating hidden Markov model with precomputation

Yang, Chunlin; Gidófalvi, Gyözö

doi:10.1080/13658816.2017.1400548

Cited by 173 publications

(79 citation statements)

References 25 publications

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“…The moving trajectory of a user on the road network can be recorded using GPS-enabled devices. Due to instrumental inaccuracies, the sampled trajectory points may not be well aligned with the locations in L. Following [33], we can preform the procedure of map matching for aligning trajectory points with locations in L. Definition 3. Trajectory.…”

Section: Preliminariesmentioning

confidence: 99%

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Wang

Zhao

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users' route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A * algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

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Section: Preliminariesmentioning

confidence: 99%

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Wang

Zhao

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…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. Qi et al [7] put forward a junction decision domain model, which is used to improve the map-matching algorithm based on the HMM.…”

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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“…In general, the map-matching accuracy is evaluated by measuring the similarity between the mapmatching result and corresponding ground-truth. Currently, various evaluation metrics have been proposed for map-matching, including the correct road identification % [92,99,103,117,139,153], map-matching precision/recall [8], route mismatch fraction [47,90,115] and matching point/route accuracy [7,152]. There is still no consensus on which metrics can better evaluate the performance [72,74], and none of the existing work evaluates their differences.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…A simple route-based metric, which is adopted in various papers [112,136], regards the matching route MR(T r) as a set of road edges ME (same applies to the ground-truth MR * (T r)) and evaluate the set precision/recall/Fmeasure (similar to the definition above). Alternatively, we can generate an overall score to indicate the accuracy [152], which is similar to the format of the Jaccard Similarity: In fact, most of the early works used the metric "Correct Road Identification%" to evaluate the percentage of roads in ground-truth that are correctly identified. It is also a route-based metric; however, this metric is never formally defined by any of them, which makes it inappropriate to simply compare their performance based on the claimed accuracies, not to mention the variety of datasets on which the experiments are conducted.…”

Section: Route-based Metricsmentioning

confidence: 99%

A Study on map-matching and map inference problems

Chao¹

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The recent upsurge of GPS(Global Positioning System)-equipped devices has enabled the tracking of users'/vehicles' locations. Meanwhile, the emergence of various location-based services emphasises the crucial role of the underlying digital map. However, the intrinsic inaccuracy of both maps and GPS trajectories has been a major issue that hinders the development of location-based applications, like navigation, location-based recommendation and traffic analysis. Several techniques are proposed to deal with the data quality issues in maps and GPS trajectories, respectively: (1) To avoid the excessive cost of ground surveying for map construction, the map inference algorithm aims to construct a digital map from GPS trajectories automatically. (2) To ensure the recency of digital maps, the map update algorithm focusses on updating an existing map using recent GPS trajectories. (3) To get rid family), Dr.

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Fast map matching, an algorithm integrating hidden Markov model with precomputation

Cited by 173 publications

References 25 publications

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

A Study on map-matching and map inference problems

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