Genetic-Inspired Map Matching Algorithm for Real-Time GPS Trajectories

Singh, Saravjeet; Singh, Jaiteg; Sehra, Sukhjit Singh

doi:10.1007/s13369-019-04247-1

Cited by 18 publications

(3 citation statements)

References 51 publications

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“…For example, attempting to solve the problem of match uncertainty caused by varying map densities, Quddus and Washington (2015) proposed a new shortest path and vehicle trajectory aided map-matching (stMM) algorithm to match low-frequency GPS data on a road map. In more recent work, Singh et al (2020) applied a novel genetic algorithm to evaluate both sparse and dense GPS data for map matching. DMM, a fast map matching framework for cellular data that uses a recurrent neural network (RNN), has also been proposed to find the most likely road path given a sequence of cell towers (Singh et al, 2020).…”

Section: Map-matchingmentioning

confidence: 99%

“…In more recent work, Singh et al (2020) applied a novel genetic algorithm to evaluate both sparse and dense GPS data for map matching. DMM, a fast map matching framework for cellular data that uses a recurrent neural network (RNN), has also been proposed to find the most likely road path given a sequence of cell towers (Singh et al, 2020). This method contrasts the commonly used Hidden Markov Models (HMM), which incur heavy computational overhead and scale poorly on large datasets.…”

Section: Map-matchingmentioning

confidence: 99%

See 1 more Smart Citation

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Shaygan¹,

Meese²,

Li³

et al. 2022

Transportation Research Part C: Emerging Technologies

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Section: Map-matchingmentioning

confidence: 99%

Section: Map-matchingmentioning

confidence: 99%

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Shaygan¹,

Meese²,

Li³

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…These differences per mode of transportation stem from the difference in maximum speed, i.e., more candidate routes and positions have to be calculated given a certain time interval. For comparison, the execution times reported in recent literature vary between 39 ms and 5.62 s, depending on the algorithm's settings and the input sample interval [52]- [54].…”

Section: ) Execution Timementioning

confidence: 99%

Map Matching and Lane Detection Based on Markovian Behavior, GIS, and IMU Data

Trogh

Botteldooren

Coensel

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.

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