Traffic optimisation scenario
It is a normal weekday morning. Carmen wakes and plans her travel for the day. She wants toleave for work in half an hour and asks
AmI, by means of a voice command, to find a vehicle toshare with somebody on her route to work.
AmI starts searching the trip database and, afterchecking the willingness of the driver, finds someone that will pass by in 40 minutes. The in-vehicle biosensor has recognised that this driver is a non-smoker – one of Carmen requirementsfor trip sharing. From that moment on, Carmen and her driver are in permanent contact if wanted(e.g. to allow the driver to alert Carmen if he/she will be late). Both wear their personal areanetworks (PAN) allowing seamless and intuitive contacts.
While taking her breakfast coffee Carmen lists her shopping since she will have guests for dinnertonight. She would like also to cook a cake and the e-fridge flashes the recipe. It highlights theingredients that are missing milk and eggs. She completes the shopping on the e-fridge screenand asks for it to be delivered to the closest distribution point in her neighbourhood. This can be ashop, the postal office or a franchised nodal point for the neighbourhood where Carmen lives. Allgoods are smart tagged, so that Carmen can check the progress of her virtual shoppingexpedition, from any enabled device at home, the office or from a kiosk in the street. She can beinformed during the day on her shopping, agree with what has been found, ask for alternatives,and find out where they are and when they will be delivered.
Forty minutes later Carmen goes downstairs onto the street, as her driver arrives. When Carmengets into the car, the VAN system (Vehicle Area Network) registers her and by doing that shesanctions the payment systems to start counting. A micro-payment system will automaticallytransfer the amount into the e-purse of the driver when she gets out of the car.
In the car, the dynamic route guidance system warns the driver of long traffic jams up ahead dueto an accident. The system dynamically calculates alternatives together with trip times. Onesuggestion is to leave the car at a nearby ‘park and ride’ metro stop. Carmen and her driver parkthe car and continue the journey by metro. On leaving the car, Carmen’s payment is deductedaccording to duration and distance.
Out of the metro station and whilst walking a few minutes to her job, Carmen is alerted by herPAN that a Chardonnay wine that she has previously identified as a preferred choice is onpromotion. She adds it to her shopping order and also sets up her homeward journey with herwearable. Carmen arrives at her job on time.
On the way home the shared car system senses a bike on a dedicated lane approaching anintersection on their route. The driver is alerted and the system anyway gives preference to bikes,so a potential accident is avoided. A persistent high-pressure belt above the city for the last tendays has given fine weather but rising atmospheric pollutants. It is rush hour and the trafficdensity has caused pollution levels to rise above a control threshold. The city-wide engine controlsystems automatically lower the maximum speeds (for all motorised vehicles) and when the carenters a specific urban ring toll will be deducted via the Automatic Debiting System (ADS).
Carmen arrives at the local distribution node (actually her neighbourhood corner shop) where shepicks up her goods. The shop has already closed but the goods await Carmen in a smart deliverybox. By getting them out, the system registers payment, and deletes the items from her shoppinglist. The list is complete. At home, her smart fridge screen will be blank.
Coming home,
AmI welcomes Carmen and suggests to telework the next day: a bigdemonstration is announced downtown.
ISTAG. Scenarios for Ambient Intelligence in 2010. EC - Community Research - IST 2001. Available from <
https://www.researchgate.net/publication/262007900_Scenarios_for_ambient_intelligence_in_2010 >. access on 5 December 2019.
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GregorioIvanoff - 06 Dec 2019
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