SplunkLive! Munich 2018: Car Sharing Fleet Demand - BMW
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Eduard Saller presented on forecasting demand for a car sharing fleet. The presentation discussed (1) using machine learning models like random forest regressors on historical vehicle pickup and drop-off data along with other data sources to capture trends and forecast demand, (2) monitoring fleet coverage gaps in real-time, and (3) using the forecasts to optimally distribute vehicles to improve availability and reduce idle time. Future work could include incorporating more context data and expanding the approach to other applications like electric vehicle charging infrastructure planning.
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The Haar Cascades Classifier algorithm can detect an empty parking slot by utilizing a parking symbol object installed in each slot parking. From the test results using the number different images and number of satage then you can concluded that the accuracy of the detection of Haar Cascade Classifier is affected by balance between the number of images trained and the number of stage used to train.
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Boomerang
This marketing proposal outlines strategies and services to promote Boomerang Systems, an automated parking company. The objectives are to educate decision-makers about robotic parking technology and establish thought leadership. The strategy involves media relations through news coverage, events and online content. Services proposed include developing feature stories, managing social media, and integrating Boomerang's web presence. A $12,000 monthly retainer is proposed for content production. Research includes potential media outlets and competitors in the automated parking industry.
Localiza institucional eng final
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Deal of the month tom tom acquired autonomos
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- 7. 7 Vehicle Data Event / Holidays e.g. Football Games Public Transport Data e.g. Metro Stations Weather Data Input Data Layers04
- 8. Car Demand Forecast ► Capture seasonal, daily and hourly trends ► Live monitoring on the map of pick-up and car-drop events ► Live monitoring of fleet coverage gaps Splunk ML Toolkit Solution04
- 9. ► Non-linear Algorithm ► Random Forest Regressor ► Historical Data ► 8 days, 1 month, etc. ► Model Input ► Car-pickup/Car-drop events ► Latitude and longitude ► Features of time ► Model Output ► Car demand Machine Learning Approach04
- 12. COMBINED WITH MAINTENANCE 01 02 CARS REALLOCATE THEMSELVES 03 STAFF USERS AUTONOMOUS DRIVING FUTURE INCENTIVES e.g. free driving minutes Types of Repositioning05
- 13. Including more data sources such as concerts, football games, holidays, etc. What’s Next?05 Fleet size prediction for cities without a car sharing service Optimal fleet size / saturation of fleet Same approach for electric cars availability and charging station distribution IMPROVE EXPAND ENHANCED PROFITABILITY FUTURE PROOF
- 14. © 2018 SPLUNK INC. KEY Takeaways Improve distance / time to cars by optimal distribution of assets
- 15. SplunkLive! MACHINE LEARNING It’s not magic, it’s just a tool. But it’s a really important tool.