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.

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

Tortora, M
;
Cordelli, E;Soda, P
2022-01-01

Abstract

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.
2022
Road traffic; Global Positioning System; Trajectory tracking; Libraries; Intelligent transportation systems; Routing; Hidden Markov models; Python; Vehicle routing; Intelligent transportation systems; map-matching; OpenStreetMap; Python; library; hidden Markov model; global navigation satellite system; trajectory
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/71864
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