Survey on vehicle map matching techniques

Huang, Zhengfeng; Qiao, Shaojie; Han, Nan; Yuan, Changan; Song, Xuejiang; Xiao, Yao

doi:10.1049/cit2.12030

Cited by 31 publications

(15 citation statements)

References 42 publications

(76 reference statements)

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“…The remaining 16% had frequencies between 1 min and 5 min long (less than 3% of waypoints in our dataset had more than 5-min sampling frequency) (Figure 1c, the horizontal axis applies the power of universal constant). This indicates that overall, this dataset was characterized by a high sampling frequency (Huang et al, 2021). For the accuracy of our speed estimation results, waypoints that had more than 90 s sampling fre- | 1129…”

Section: Road Typementioning

confidence: 88%

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

Zhang

Stewart

2023

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With the increasing availability of location‐aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona‐based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24‐h period.

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Section: Road Typementioning

confidence: 88%

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

Zhang

Stewart

2023

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“…The map matching method plays an important role in the preprocessing step of trajectory data mining. In practice, vehicles usually report their GPS positions to the dispatch center at a lower sampling rate in order to save communication and energy costs [26][27][28]. If the processing object is high-frequency trajectory data, where the distance between adjacent location points is short, the road network topology can be used for analysis and matching [29].…”

Section: Discussionmentioning

confidence: 99%

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

Chen

et al. 2022

IJGI

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Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms.

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“…Given the growth of map-matching techniques applied in different scenarios, different taxonomies have been proposed to categorise them in accordance with the different properties to be highlighted. In [4], the authors proposed a frequency-based taxonomy that categorises map-matching techniques into three different groups based on the sampling frequency of the recorded geographical coordinates:…”

Section: Map-matching: a Quick Overviewmentioning

confidence: 99%

“…• Advanced techniques: Advanced techniques concern the use of novel and more refined concepts such as a Kalman Filter, Dempster-Shafer's mathematical theory of evidence, fuzzy logic methods, neural networks, reinforcement learning, genetic algorithms and so on, to find an optimal matching path. Finally, map-matching techniques can be divided into incremental and global methods according to the number of sampling points considered during the matching process [4]. The former sequentially processes the points one-by-one, whilst the latter takes into account the whole trajectory of the sampled data point to determine the matched one.…”

Section: Map-matching: a Quick Overviewmentioning

confidence: 99%

“…), the acquired trajectories diverge from the actual ones. Consequently, to guarantee high-quality trajectory data to ITS applications, it is first required to align the GPS sampling data to the actual underlying driving routes network layer, a process known as map-matching [4]. Indeed, the main objective of mapmatching algorithms is to reconstruct the correct sequence of road segments the vehicle is travelling on by integrating the positioning data with the spatial road network data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction

2022

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The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at https://github.com/cosbidev/PyTrack or the official documentation at https://pytrack-lib.readthedocs.io.

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Survey on vehicle map matching techniques

Cited by 31 publications

References 42 publications

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction

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