This is an interdisciplinary proposal aimed mainly at bus operators and public and private managers of mobility in different cities. The technology that has been used in the project can be applied to any market and has high potential scope in companies that manage passenger transport lines.\r
The project is in implementation phase and is already functioning in the Regional Urban Transport (TUC) of Pamplona. In addition, it is expected to be incorporated into the urban and interurban lines of other cities.
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21417
<\/p>\n\n\n\n
17\/02\/2023
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The mobility of the future must be more intelligent, sustainable and connected. In this context, the project \u2018Solutions for smart mobility\u2019, with the participation of the Laboratory of the Barcelona School of Informatics (inLab FIB) of the Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC), has developed a predictive model for the occupation of passengers in public transport buses based on deep learning methods.\r
The project is based on the context of the pandemic due to COVID-19 in which public transport operators wanted to minimise journeys with a large number of passengers, for two reasons: to reduce infections and to recover users\u2019 confidence in public transport.\r
Data-driven methods have been used based on diverse sources (ticketing or counting ticket validations, the calendar and cameras inside buses). Specifically, classical methods have been studied for the treatment of time series, such as the ARIMA method, and algorithms of neural networks that have enabled modelling of future employment, based on historical data and incorporating variables and important external attributes such as the school calendar.\r
Through a platform, a mobile application, the user can consult the estimated occupation of buses throughout the day. In this way, the aim is for users of the bus service to choose the emptiest bus lines, and in this way to balance the offer and demand for the service and avoid crowds in public transport. Another way of accessing the results is through a web platform where the results of these predictions can be viewed.\r
A collaborator in the project is the company Ityneri by GeoActio, which develops smart solutions for the modernisation of public transport.
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Results and impact<\/h4>\r
This is an interdisciplinary proposal aimed mainly at bus operators and public and private managers of mobility in different cities. The technology that has been used in the project can be applied to any market and has high potential scope in companies that manage passenger transport lines.\r
The project is in implementation phase and is already functioning in the Regional Urban Transport (TUC) of Pamplona. In addition, it is expected to be incorporated into the urban and interurban lines of other cities.
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tecnologias-de-las-tic-en
date
DESC
21417
<\/p>\n\n\n\n
17\/02\/2023
Project Header
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The mobility of the future must be more intelligent, sustainable and connected. In this context, the project \u2018Solutions for smart mobility\u2019, with the participation of the Laboratory of the Barcelona School of Informatics (inLab FIB) of the Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC), has developed a predictive model for the occupation of passengers in public transport buses based on deep learning methods.\r
The project is based on the context of the pandemic due to COVID-19 in which public transport operators wanted to minimise journeys with a large number of passengers, for two reasons: to reduce infections and to recover users\u2019 confidence in public transport.\r
Data-driven methods have been used based on diverse sources (ticketing or counting ticket validations, the calendar and cameras inside buses). Specifically, classical methods have been studied for the treatment of time series, such as the ARIMA method, and algorithms of neural networks that have enabled modelling of future employment, based on historical data and incorporating variables and important external attributes such as the school calendar.\r
Through a platform, a mobile application, the user can consult the estimated occupation of buses throughout the day. In this way, the aim is for users of the bus service to choose the emptiest bus lines, and in this way to balance the offer and demand for the service and avoid crowds in public transport. Another way of accessing the results is through a web platform where the results of these predictions can be viewed.\r
A collaborator in the project is the company Ityneri by GeoActio, which develops smart solutions for the modernisation of public transport.
Project Header
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20px
Results and impact<\/h4>\r
This is an interdisciplinary proposal aimed mainly at bus operators and public and private managers of mobility in different cities. The technology that has been used in the project can be applied to any market and has high potential scope in companies that manage passenger transport lines.\r
The project is in implementation phase and is already functioning in the Regional Urban Transport (TUC) of Pamplona. In addition, it is expected to be incorporated into the urban and interurban lines of other cities.
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Digital Transformation<\/a>\r
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tecnologias-de-las-tic-en
date
DESC
21417
<\/p>\n\n\n\n
17\/02\/2023
Project Header
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The mobility of the future must be more intelligent, sustainable and connected. In this context, the project \u2018Solutions for smart mobility\u2019, with the participation of the Laboratory of the Barcelona School of Informatics (inLab FIB) of the Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC), has developed a predictive model for the occupation of passengers in public transport buses based on deep learning methods.\r
The project is based on the context of the pandemic due to COVID-19 in which public transport operators wanted to minimise journeys with a large number of passengers, for two reasons: to reduce infections and to recover users\u2019 confidence in public transport.\r
Data-driven methods have been used based on diverse sources (ticketing or counting ticket validations, the calendar and cameras inside buses). Specifically, classical methods have been studied for the treatment of time series, such as the ARIMA method, and algorithms of neural networks that have enabled modelling of future employment, based on historical data and incorporating variables and important external attributes such as the school calendar.\r
Through a platform, a mobile application, the user can consult the estimated occupation of buses throughout the day. In this way, the aim is for users of the bus service to choose the emptiest bus lines, and in this way to balance the offer and demand for the service and avoid crowds in public transport. Another way of accessing the results is through a web platform where the results of these predictions can be viewed.\r
A collaborator in the project is the company Ityneri by GeoActio, which develops smart solutions for the modernisation of public transport.
Project Header
no-repeat;left top;;
auto
20px
Results and impact<\/h4>\r
This is an interdisciplinary proposal aimed mainly at bus operators and public and private managers of mobility in different cities. The technology that has been used in the project can be applied to any market and has high potential scope in companies that manage passenger transport lines.\r
The project is in implementation phase and is already functioning in the Regional Urban Transport (TUC) of Pamplona. In addition, it is expected to be incorporated into the urban and interurban lines of other cities.
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Logistics and Mobility<\/a>\r
Digital Transformation<\/a>\r
\u00a0
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