In a digital, multi-modal system, a full day's mobility is easy. A map system requests information from a user's calendar. The calendar data is used to predicts meeting & transit times. Actual meeting times update those estimates, live—and biosensors inform suggested travel time for biking and walking. Travel time, distance and resource usage converts to transit tickets with itinerary, cost, passenger, baggage and other metadata embedded. And vehicles and infrastructure sensors coordinate for handoff, decision-making, customization and efficiency. Let's start at the beginning of a day of digital automobility to actually experience this.

Jenna's alarm clock goes off. Lights come on in her apartment, the climate control systems adjust for her comfort, and coffee starts brewing. "Estimated transit time is a little higher than normal this morning. Would you like to get ready quicker, take fast-lane transit, or delay the beginning of your staff meeting?," prompts the digital assistant over the sound system to Jenna in the kitchen.

Jenna's assistant recommends she starts with a trip on a mesh cycle, co-owned as part of a citywide cooperative that is automatically, digitally managed. The cycle has electric assist, so Jenna can pedal as much or as little as she desires, and the cycle selected for her by the system is sufficiently charged to get her to her next shift. She rides it about 3/4 of a mile, per the on-bike display.

She drops her after-work bag on the baggage receptacle next to the bus entrance, and it will automatically be routed independently of her trips throughout the day until it is needed.

Her next shift is stepping off the cycle next to a bus owned by her company which will take her to the company campus. When she’s on the bus, her laptop and mobile devices automatically suggest which documents she might want to work on based on the quality and security of connectivity along the route and in the bus at any given time.

Later in the day, she needs to go to another meeting that is in a congested area, so the mapping system suggest she use a public train. Her headset tells her which train to go on and which stop to get off at.

Her seat is reserved for her. It is pre-heated to her desired temperature, as it's a colder day than normal.

When she gets off at the appointed stop, she walks off the train platform to the scooter stand. Her pre-selected scooter is flashing with her signature color and pattern, and her playlist shifts from her headset to the handlebars. Her mobile devices charge automatically in the cargo compartment and the controls are re-routed to the handlebars—her briefcase automatically charges in proximity to the charging plate.

Partway through her trip, guided on the helmet display with turn-by-turn directions, an alert comes up to let her know that rain is coming. She is rerouted to the closet scooter stand, and a traditional taxi is summoned to pick her up, arriving just as she has parked and taken her bag out of the cargo compartment. The scooter locks and begins charging automatically.  

The analog driver has received manual instructions for the climate control and automatic navigation direction has already begun. Her in-car phone connection routes Jenna's playlist to the audio system in the back seats—skipping songs which might be inappropriate for a shared space or annoying the driver. Partway through the trip, a request comes in to pick up another passenger, and both the driver and Jenna quickly tap 'yes' to accept the ride; both get 'karma' points. Jenna's fare is reduced, and the driver's payment is a bit higher. Because the other passenger works for Jenna's company's competitor and does not have a compatible confidentiality certificate, sensitive incoming work phone calls are automatically declined, but suggested topics for discussion pop up on each person's display before they're both in the car together. No one in the vehicle has to manage this.

When she gets out, she and the driver are automatically reminded of her bag in the storage area and a followup meeting option is sent to each passenger to accept or decline.

After her shareholder lunch meeting, Jenna leaves her office. Because her lunch was rather large, she opts to walk, and her estimated time of arrival is automatically predicted based on her pace and heart rate. The climate controls in her next office automatically adjust down so that she doesn't feel overheated when she arrives, and automatically recalibrate after a few moments.

After her doctor's appointment, she heads to the grocery store, which the vehicle already mapped. She swipes to begin and settle back in her seat. The vehicle stops automatically to pick up Jenna's colleague, who is going to a nearby destination, and they meet in the car; meeting notes are automatically transcribed, uploaded to Jenna's cloud, and scrubbed from the car's internal memory. 

When it's time for an afternoon doctor's appointment, Jenna walks outside to the parking lot and steps up to one of her favorite cars. The vehicle unlocks as she approaches, and the seats, audio, temperature and window tint have all automatically shifted for her. The vehicle takes off for its destination on an autonomous route, reaching speeds of up to 300 km/h and drafting nearby autonomous vehicles less than a meter in front of it for efficiency. When the vehicle exits the autoroute, Jenna taps manual mode after putting on the augmented reality goggles on the dashboard, and takes a curvy back road for fun. The walls and frame of the car disappear whenever she looks at them, with camera-driven outside views automatically superimposed, so she has no 'blind spots,' and even though there aren't many sensors on this old back-country road, the car's advanced microphones sense oncoming vehicles on blind corners and alerts her and preps the brakes. Even when they aren't connected cars, like an old tractor she encounters along the way, the margin of safety is vastly increased through her vehicle's smart systems.

When Jenna walks back out to the car after grocery shopping, an alert pops up letting her know her friend Jessica is nearby. She swipes to reroute, because she has a free spot in her calendar, and they have coffee together.

While at coffee, her mobile alerts her about a package her brother needs picked up. She accepts on the display, and the merchant pre-loads the package in the carpark, next to the groceries being cooled by the AC system, and Jenna heads home. Her brother knows she is in the neighborhood and comes over to pick up the package and join for dinner (she shared her grocery list with him and got excited to help bake grandmother's favorite bread with her).

At the end of the day, dinner is on the table, Jenna is relaxed, and a lot got accomplished. Jenna’s mind was free to do her most important work, socialize and relax, rather than spend time, energy and anxious attention on getting from place to place.

Throughout the day, the payment platforms for all of the transit methods corresponded with each other and dynamically traded resources. When Jenna chose to take her manual-driving detour, she used more energy than the default autonomous system and was automatically charged fractionally more for the energy, tire and brake wear. But because she'd shared a ride earlier in the day, the impact was offset, and her eco score and resource charges ended up balancing out.

The system also predicted that her schedule was relatively light on Friday and that many people were staying in town over the weekend, so it offered her a great 'getaway' deal and automatically charged the trip, rescheduled her Friday appointments based on their projected impact on the software team she would be meeting with and told her friends in the town she'd be visiting that she'd be coming. One volunteered to host her, and Jenna's system automatically updated her friend's grocery list with her food allergies so they could have a great dinner party over the weekend.