A dynamic two-dimensional (D2D) weight-based map-matching algorithm

Sharath, M.N.; Velaga, Nagendra R.; Quddus, Mohammed

doi:10.1016/j.trc.2018.12.009

Cited by 43 publications

(23 citation statements)

References 51 publications

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“…Naïve weighting A group of algorithms [13,15] apply the weight without using a particular model. Instead, they simply assign a group of candidates to each trajectory segment (or location observation) and find a road edge from each group that maximizes the predefined scoring function.…”

Section: Scoring Modelmentioning

confidence: 99%

“…The found segment in every timestamp is either returned if applied to the online scenario or waited to be joint with other matched segments if applied in the offline scenario. Most recent work in this category [15] achieves a lane-level map-matching performance. The algorithm first identifies lanes in each road by utilising the road width information in the map and partition them into grids accordingly.…”

Section: Scoring Modelmentioning

confidence: 99%

“…Our experiments are performed on a single server with two Intel(R) Xeon(R) CPU E5-2630 with 10 cores/20 threads at 2.2GHz each, 378GB memory and Ubuntu 16.04. Both the route matching result MR(T r) and the corresponding ground-truth are regarded as sets of road edges and are evaluated by F-measure, which is commonly used in map-matching evaluation [15,19]. The candidate mapmatching algorithms used in the experiments include the most popular offline HMM map-matching [11], the most-recent offline WGT algorithm [20] and an online Scoring method [13].…”

Section: Experimental Settingsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Map-Matching Algorithms

Chao

Hua

et al. 2020

Lecture Notes in Computer Science

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The map-matching is an essential preprocessing step for most of the trajectory-based applications. Although it has been an active topic for more than two decades and, driven by the emerging applications, is still under development. There is a lack of categorisation of existing solutions recently and analysis for future research directions. In this paper, we review the current status of the map-matching problem and survey the existing algorithms. We propose a new categorisation of the solutions according to their map-matching models and working scenarios. In addition, we experimentally compare three representative methods from different categories to reveal how matching model affects the performance. Besides, the experiments are conducted on multiple real datasets with different settings to demonstrate the influence of other factors in map-matching problem, like the trajectory quality, data compression and matching latency.

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Section: Scoring Modelmentioning

confidence: 99%

Section: Scoring Modelmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Map-Matching Algorithms

Chao

Hua

et al. 2020

Lecture Notes in Computer Science

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show abstract

“…It features a favorable matching effect for high-sampling-frequency trajectories (one trajectory point or more can be matched on one road), but it has difficulty ensuring a high matching accuracy for low-sampling-frequency trajectories. To enhance matching accuracy, some new methods have been developed, such as topology map matching [5,8], spatial-temporal feature-based map matching [2,12], and weight-based map matching [13][14][15][16][17]. In a study by Brakatsoulas et al [8], an incremental matching method has been proposed using the "Look-Ahead" matching strategy.…”

Section: Local 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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“…Hence, the input of a map-matching process consists of both the trajectory and the road network, which are defined in Section 1. [71,112].…”

Section: Preliminariesmentioning

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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A dynamic two-dimensional (D2D) weight-based map-matching algorithm

Cited by 43 publications

References 51 publications

A Survey on Map-Matching Algorithms

A Survey on Map-Matching Algorithms

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

A Study on map-matching and map inference problems

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