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| Tekkaya, A. Erman |
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| Förster, Peter |
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| Mudimu, George T. |
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| Shibata, Lillian Marie |
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| Talabbeydokhti, Nasser |
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| Laffite, Ernesto Dante Rodriguez |
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| Schöpke, Benito |
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| Gobis, Anna |
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| Alfares, Hesham K. |
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| Münzel, Thomas |
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| Joy, Gemini Velleringatt |
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| Oubahman, Laila |
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| Filali, Youssef |
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| Philippi, Paula |
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| George, Alinda |
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| Lucia, Caterina De |
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| Avril, Ludovic |
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| Belachew, Zigyalew Gashaw |
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| Kassens-Noor, Eva | Darmstadt |
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| Cho, Seongchul |
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| Tonne, Cathryn |
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| Hosseinlou, Farhad |
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| Ganvit, Harsh |
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| Schmitt, Konrad Erich Kork |
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| Grimm, Daniel |
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Quesada-Arencibia, A.
Universidad de Las Palmas de Gran Canaria
in Cooperation with on an Cooperation-Score of 37%
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Publications (4/4 displayed)
- 2022Data mining methodology for obtaining epidemiological data in the context of road transport systemscitations
- 2014Using Massive Vehicle Positioning Data to Improve Control and Planning of Public Road Transportcitations
- 2011Surface Classification for Road Distress Detection System Enhancementcitations
- 2011Monocular Vision-based Target Detection on Dynamic Transport Infrastructurescitations
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document
Using Massive Vehicle Positioning Data to Improve Control and Planning of Public Road Transport
Abstract
This study describes a system for the automatic recording of positioning data for public transport vehicles used on roads. With the data provided by this system, transportation-regulatory authorities can control, verify and improve the routes that vehicles use, while also providing new data to improve the representation of the transportation network and providing new services in the context of intelligent metropolitan areas. The system is executed autonomously in the vehicles, by recording their massive positioning data and transferring them to remote data banks for subsequent processing. To illustrate the utility of the system, we present a case of application that consists of identifying the points at which vehicles stop systematically, which may be points of scheduled stops or points at which traffic signals or road topology force the vehicle to stop. This identification is performed using pattern recognition techniques. The system has been applied under real operating conditions, providing the results discussed in the present study.
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