Explorateur de ressources

First funded by Bloomberg Philanthropies and developed in collaboration with NACTO and the Open Transport Partnership, SharedStreets is neutral, anonymized clearinghouse for data collected by transportation providers, private companies and government agencies, as well as a hub for industry-leading data analysis, traffic planning, street design and development of new technologies. Already operating in over 30 cities around the world, the platform overcomes long-standing legal, regulatory and technological barriers between the public and private sectors by converting today’s ad hoc, disparate transportation data sources into a mutually readable, shared, global standard for the first time. It introduces a universal language for digitally describing every aspect of city streets, opening new markets to private sector innovators and eliminating the need to manually clean, collate and transfer data sources, saving crucial public funds. Empowering cities to measure the impacts and outcomes of micromobility by creating tools for turning large datasets into key metrics. The mobility metrics software aggregates data to generate insights about mobility while protecting individual privacy. It standardizes the conversion of data from points (like where a trip starts and where a trip ends) into trip information and then aggregates trips over time. The tool produces overall daily metrics as well as more detailed metrics for particular areas and time periods. [https://sharedstreets.io/taxi-tnc-activity/ Taxi and TNC Activity] Empower cities to better manage curb space with high resolution anonymized and aggregated data on for-hire vehicle pick-up and drop-off, while protecting user privacy. Cities have access to curb pick-up and drop-off data from Uber and Lyft to allow them to make policy and infrastructure decisions based on the use of their streets. '''Tools and projets:''' * [https://github.com/sharedstreets Sharedstreets on Github] * Pilots: [https://sharedstreets.io/mobility-metrics/ Mobility metrics] * [https://github.com/sharedstreets/curblr CurbLR] CurbLR is a data standard for describing urban curb regulations. It exists to help government effectively manage and regulate the curb and to support public and private users of city streets.

Fournit du conseil, expertise, développement et formations autour de la technologie MediaWiki et Semantic MediaWiki.

Créé à l'initiative de l'ADMR de Plélan-le-Grand en 2017. Nous aidons les associations et collectivités à proposer leur service de transport solidaire en ligne

Identification et signalétique des lieux de prise en charge pour homogénéiser les zones de rencontre et avoir un meilleur maillage. '''Intérêt ''' * Besoin de connaître l’usages des aires ainsi que l’usage des applications. Par exemple, connaitre les zones d'encombrement, proposer d'autres lieux plus pratiques * Voir les aires aménagées vs lieux effectivement utilisés/pratiques pour l'usage. Il y a des aires non utilisées, mises en places par un département sans étude pratique (trop théorique) * Type d’usage : Quel est l’arrêt de covoiturag le plus proche ? What is the nearest POI covoiturage * Etre capable de proposer aux acteurs publics un format de donnée.

Sur cette page internet, sont publiés les données et une carte permettant de visualiser les aires de covoiturage en France. Les Points de rencontre covoiturage sont identifiés et références grâce à une base de données des lieux en open data, format de référence et formulaire ! https://doc.transport.data.gouv.fr/producteurs/points-de-rencontre-de-covoiturage

* [http://www.general-vision.com/neuromem/nm500/ Digital neural network of 576 neurons] * Expandable to 4032 neurons with add-on NeuroBrick module * Arduino connectors * USB_C connector * Power source through Arduino connector or micro USB * [https://www.general-vision.com/documentation/TM_NeuroShield_GettingStarted.pdf Read more…] * [https://www.general-vision.com/documentation/TM_NeuroMem_Library_Arduino.pdf NeuroMem library] to access the neurons via SPI, teach them in real-time and and start monitoring immediately. * [https://www.general-vision.com/techbriefs/TB_TestNeurons_SimpleScript.pdf Academic scripts] (Arduino IDE and Python for RPi) * [https://www.general-vision.com/techbriefs/TB_NeuroMemArduino_IMUDemo.pdf Motion recognition examples] using the on-board IMU from Invensense (MPU6050) and the IMU from the Arduino101. * [https://www.general-vision.com/techbriefs/TB_NeuroMemArduino_VideoMonitoringDemo.pdf Video recognition examples] using an ArduCAM shield and the RaspiCam

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