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{"id":1443,"date":"2020-04-08T13:42:43","date_gmt":"2020-04-08T12:42:43","guid":{"rendered":"https:\/\/trackandknowproject.eu\/?page_id=1443"},"modified":"2020-04-08T13:48:33","modified_gmt":"2020-04-08T12:48:33","slug":"pilots","status":"publish","type":"page","link":"https:\/\/trackandknowproject.eu\/pilots\/","title":{"rendered":"Pilots"},"content":{"rendered":"\n<p>The Track &amp; know\nbig data platform and developed toolboxes are used to answer a variety of\nbusiness questions in three different pilots. Questions within these three\npilots are as follows:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>BIG DATA IN <\/strong><strong>AUTO\nINSURANCE &amp; INNOVATIVE MOBILITY<\/strong><strong> SERVICES<\/strong><\/h4>\n\n\n\n<p>In-depth and accurate crash probability\nestimation, cost-benefit analysis of switching to electric mobility, carpooling\npotential and their underlying benefits.<\/p>\n\n\n\n<p>Several studies have shown\nthat the new business models \u201cPay as much as you drive\u201d and \u201cPay how you drive\u201d\ndefinitely need telematics in order to collect data and get relevant semantics\nfrom it. <\/p>\n\n\n\n<ul><li>With <strong>support for computing intensive, analytic\nprocessing and machine learning techniques<\/strong>, BDA develop a model that\nprovide the likelihood to occur a car crash given certain condition and how\nthis information could be used to guide the drivers\u2019 behaviour in order to\nachieve decreasing driving risk condition and consequently decreasing insurance\nfees.<strong><\/strong><\/li><li>with support of data enrichment, analytic\nprocessing and machine learning, BDP and BDA will provide methods that estimate\ndriver\u2019s saving in terms of money and time when switched to <strong>electric vehicle<\/strong>, keeping driver\u2019s\nusual habits or usual paths, charging point availability and charging time.<\/li><li>&nbsp;with\nsupport of data enrichment, analytic processing and machine learning, BDP and\nBDA will provide methods that estimate clusters of journeys by their\nspatiotemporal similarities to make recommendations for <strong>car-pooling<\/strong>.<strong><\/strong><\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>BIG DATA IN HEALTHCARE SERVICES<\/strong><\/h4>\n\n\n\n<p>Medical service optimisation to improve response time and reduction of unnecessary travel, diagnoses potential based on exploration of driver behaviour.<\/p>\n\n\n\n<p><strong>Fact:<\/strong> 1.5 million adults in the UK have Obstructive Sleep Apnoea (OSA) but only\n330,000 patients are diagnosed and receiving treatment. OSA patients are around\n3 times more likely to be involved in a crash than a regular driver and\nSleepiness whilst driving accounts for around 20% of all motor vehicle\ncollisions <\/p>\n\n\n\n<ul><li><strong>Problem 1: Royal Papworth Hospital\u2019s<\/strong><strong> (Cambridge, UK)<\/strong><strong> patients have to\nperform at least 4 long journeys to get access to the oximetry test because of\nimproper distribution of oximeters\u2019 pick-up points. The results are increased\ntravel distance, no-show rate and waiting times.<\/strong><\/li><\/ul>\n\n\n\n<p><strong>The role of big data:<\/strong> To use known risk factors of OSA and open source data to\npredict likely high OSA risk areas and redistribute oximeters to locations\nwhere most patients live, improving service quality<\/p>\n\n\n\n<ul><li><strong>Problem 2: The absence of measures to qualify whether OSA\npatients are safe to drive on the road. <\/strong><\/li><\/ul>\n\n\n\n<p><strong>The role of big data:<\/strong> To use some of the methods of tracking GPS data and\naccelerometer to understand driving behaviour of OSA patients<\/p>\n\n\n\n<ul><li><strong>Track&amp;Know success story:<\/strong> <strong>\u2018\u2018Sleep well \u2013 drive safely \u2013 a\ntale of mobility tracking and Big Data\u2019\u2019<\/strong> won \u201cSuccess Story Awards 2019\u201d at\nthe BDV PPP summit 2019 in Riga (i.e. the primary event for driving European\ninnovation in Big Data and Artificial Intelligence)<\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>BIG DATA INNOVATIONS IN FLEET MANAGEMENT<\/strong><\/h4>\n\n\n\n<p>Predictive maintenance, exploration of\nways to detect data anomaly and reduction of false alarms, cost reduction\npotential and driver behaviour improvement to reduce accidents. <strong><\/strong><\/p>\n\n\n\n<p>1. With <strong>support for computing\nintensive, analytic processing and machine learning techniques<\/strong>, BDA helps\nidentify driving behavior excess per driver, provide recommendations for fuel\nconsumption reduction based on driver behavior, identify patterns leading to\nimproved fleet maintenance costs and support preventive maintenance\nrecommendations based on tracked parameters (service downtime, tire life, etc.)\n<\/p>\n\n\n\n<p>2. With <strong>Future Location Predication<\/strong>,\nBDA helps proactively identify traffic hot spots per day and its alternative\nroutes<\/p>\n\n\n\n<p>3. With <strong>Trajectory Prediction<\/strong>,\nBDA helps provide recommendations for fuel consumption reduction based on the\noverall fleet performance optimization, provide accurate estimations of future\ntravel distances and increase the recommendations for alternative routes based\non fuel economy and road conditions. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Track &amp; know big data platform and developed toolboxes are used to answer a variety of business questions in three different pilots. Questions within these three pilots are as [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/pages\/1443"}],"collection":[{"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/comments?post=1443"}],"version-history":[{"count":3,"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/pages\/1443\/revisions"}],"predecessor-version":[{"id":1448,"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/pages\/1443\/revisions\/1448"}],"wp:attachment":[{"href":"https:\/\/trackandknowproject.eu\/wp-json\/wp\/v2\/media?parent=1443"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}