{"id":16438,"date":"2022-06-20T05:28:02","date_gmt":"2022-06-20T09:28:02","guid":{"rendered":"https:\/\/www.iotm2mcouncil.org\/?post_type=news&#038;p=16438"},"modified":"2022-06-20T05:28:03","modified_gmt":"2022-06-20T09:28:03","slug":"otonomo-introduces-ml-based-urban-planning","status":"publish","type":"news","link":"https:\/\/www.iotm2mcouncil.org\/iot-library\/news\/smart-cities-news\/otonomo-introduces-ml-based-urban-planning\/","title":{"rendered":"Otonomo introduces ML-based urban planning"},"content":{"rendered":"\n<div class=\"wp-block-image\"><figure class=\"alignleft size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.iotm2mcouncil.org\/wp-content\/uploads\/2022\/06\/otonomo.png\" alt=\"\" class=\"wp-image-16439\" width=\"326\" height=\"245\"\/><\/figure><\/div>\n\n\n\n<p>Otonomo has introduced new machine learning-based solutions for urban planning, mobility and EV adoption. The solutions leverage the firm&#8217;s proprietary machine learning and data science to create digital twin models with over 30 unique indicators to allow predictive real-world modelling.<\/p>\n\n\n\n<p>Israeli firm Otonomo\u2019s technology is designed to assess human movement, analyse real-world personas, context, intent, rider segmentation, preferences, and needs, and transform large-scale data into actionable mobility intelligence for city authorities and urban planners.<\/p>\n\n\n\n<p>According to Otonomo, its solutions fuse rich data sources that include daily, up to date, near real time physical sources from mobile devices, municipalities, charging networks, micro mobility performance. The firm says that its solutions will also soon integrate Otonomo\u2019s multi-layered connected vehicle data as well.<\/p>\n\n\n\n<p>The firm&#8217;s urban intelligence solution is designed to provide up-to-date visibility into mobility patterns in specified areas including detailed origin and destination matrices, visitation rates, and traffic flow and volume, to allow transportation and city planners to design and plan networks based on hyperlocal multimodal mobility needs. Adding sociodemographic profiles and purpose of trip creates even more value for urban planners who have previously been limited with basic transit &#8220;counting&#8221; data.<\/p>\n\n\n\n<p>The company also provides city authorities with a mobility as a service (MaaS) solution, designed to increase operational efficiency and increase ROI with access to clusters of demand for ridership through Otonomo\u2019s intelligence platform with supporting data streams and feeds from customer data lakes and applications. The solution aims to help providers of mobility services match supply with up-to-date demand data by deploying stations in optimal locations based on predictive performance metrics, reallocating their resources, and rebalancing their fleet to maximise ridership. According to Otonomo, this solution also helps cities drive modal shift by increasing service accessibility of mobility services to the people that are most likely to use them.<\/p>\n\n\n\n<p>For cities that are undergoing EV adoption, the company also provides an EV intelligence solution, designed to improve deployment of charging stations and sites, and maximise ROI and service accessibility. Otonomo can predict demand and work with EV charge point operators to pinpoint optimal locations for placing future charging stations. Data-driven EV planners gain access to accurate measurements of demand for EV charging sites in each city, precise mapping of underserved zones across primary EV corridors. This also gives EV automakers insights into which charge point operators may be best for partnerships in specific geographies.<\/p>\n\n\n\n<p>\u201cAs large cities must find solutions to liveability and sustainability issues caused by traffic congestion, noise and emissions, a more systematic use of real-time mobility data has become critical,\u201d said Claire Eln\u00e9cav\u00e9, Senior Consultant at PTOLEMUS Consulting Group. \u201cThe use of un-siloed, open-to-all data intelligence is a major part of the solution to build multi-modal transport and Mobility-as-a-Service (MaaS) and reduce the environmental footprint of transportation. Otonomo clearly aims to be part of the solution.\u201d<\/p>\n\n\n\n<p>\u201cI\u2019m excited for Otonomo to expand our offering from connected vehicle data that our customers apply so effectively in their products and services, to solutions where we apply our own data science to give new and existing customers deep actionable insights. Using our mobility intelligence platform, our customers can understand the modes of movement in their cities and reduce CO2 emissions through the use of electric vehicles, smart city planning and more accessible mobility services,\u201d said Ben Volkow, CEO and co-founder of Otonomo.<\/p>\n","protected":false},"author":26711,"featured_media":16439,"template":"","news-category":[57],"library-tag":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Otonomo introduces ML-based urban planning - IoT M2M Council<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iotm2mcouncil.org\/iot-library\/news\/smart-cities-news\/otonomo-introduces-ml-based-urban-planning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Otonomo introduces ML-based urban planning - IoT M2M Council\" \/>\n<meta property=\"og:description\" content=\"Otonomo has introduced new machine learning-based solutions for urban planning, mobility and EV adoption. 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