The Track & 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 follows:
BIG DATA IN AUTO INSURANCE & INNOVATIVE MOBILITY SERVICES
In-depth and accurate crash probability estimation, cost-benefit analysis of switching to electric mobility, carpooling potential and their underlying benefits.
Several studies have shown that the new business models “Pay as much as you drive” and “Pay how you drive” definitely need telematics in order to collect data and get relevant semantics from it.
- With support for computing intensive, analytic processing and machine learning techniques, BDA develop a model that provide the likelihood to occur a car crash given certain condition and how this information could be used to guide the drivers’ behaviour in order to achieve decreasing driving risk condition and consequently decreasing insurance fees.
- with support of data enrichment, analytic processing and machine learning, BDP and BDA will provide methods that estimate driver’s saving in terms of money and time when switched to electric vehicle, keeping driver’s usual habits or usual paths, charging point availability and charging time.
- with support of data enrichment, analytic processing and machine learning, BDP and BDA will provide methods that estimate clusters of journeys by their spatiotemporal similarities to make recommendations for car-pooling.
BIG DATA IN HEALTHCARE SERVICES
Medical service optimisation to improve response time and reduction of unnecessary travel, diagnoses potential based on exploration of driver behaviour.
Fact: 1.5 million adults in the UK have Obstructive Sleep Apnoea (OSA) but only 330,000 patients are diagnosed and receiving treatment. OSA patients are around 3 times more likely to be involved in a crash than a regular driver and Sleepiness whilst driving accounts for around 20% of all motor vehicle collisions
- Problem 1: Royal Papworth Hospital’s (Cambridge, UK) patients have to perform at least 4 long journeys to get access to the oximetry test because of improper distribution of oximeters’ pick-up points. The results are increased travel distance, no-show rate and waiting times.
The role of big data: To use known risk factors of OSA and open source data to predict likely high OSA risk areas and redistribute oximeters to locations where most patients live, improving service quality
- Problem 2: The absence of measures to qualify whether OSA patients are safe to drive on the road.
The role of big data: To use some of the methods of tracking GPS data and accelerometer to understand driving behaviour of OSA patients
- Track&Know success story: ‘‘Sleep well – drive safely – a tale of mobility tracking and Big Data’’ won “Success Story Awards 2019” at the BDV PPP summit 2019 in Riga (i.e. the primary event for driving European innovation in Big Data and Artificial Intelligence)
BIG DATA INNOVATIONS IN FLEET MANAGEMENT
Predictive maintenance, exploration of ways to detect data anomaly and reduction of false alarms, cost reduction potential and driver behaviour improvement to reduce accidents.
1. With support for computing intensive, analytic processing and machine learning techniques, BDA helps identify driving behavior excess per driver, provide recommendations for fuel consumption reduction based on driver behavior, identify patterns leading to improved fleet maintenance costs and support preventive maintenance recommendations based on tracked parameters (service downtime, tire life, etc.)
2. With Future Location Predication, BDA helps proactively identify traffic hot spots per day and its alternative routes
3. With Trajectory Prediction, BDA helps provide recommendations for fuel consumption reduction based on the overall fleet performance optimization, provide accurate estimations of future travel distances and increase the recommendations for alternative routes based on fuel economy and road conditions.