Speculative Design: Transit 2030

Imagine a “Traffic Internet”…

What if in the year 2030, Xiaomi can partner with Chinese Government to provide a subscription transportation service.

This transportation “internet” of vehicles will be more safe and accessible to parents with children, the elderly, and the handicapped.

In addition, it will be more efficient and clean, making life better in China

Transportation Network as a human-machine interface to the city

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Internet of Vehicles uses IoT technologies and Artificial Intelligence in order to create a swarm-like well choreographed transportation system

This system incorporates autonomated services like strollers for parents, wheelchair attachments for handicapped, and moving chairs.

These parts work in tandem with existing traffic network of self driving vehicles, smart lanes, and robotic parking services.

Speed controlled areas can also be energy farms.

  1. Stroller Pods

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Parents with Infants

A key insight gained from interviews with new parents who frequently use metro revealed that common pain points come from inconvenience of carrying the stroller when not in use. The Xiaomi Stroller seeks to solve that problem by taking the form of a shared autonomous scooter that a baby bassinet can attach to. The ability to relinquish the stroller allows more freedom between stops and destinations

2. Wheelchair Boosts

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Further research showed that it is difficult for wheelchair users to move around inside of a metro station. Having elevators is not a complete solution: it is tiring for a disabled person to roll themselves a further distance, if they can even locate the elevator.

The Xiaomi wheelchair booster is detachable and allows wheelchair users a powered option without the need to switch to a different wheelchair.

3. Smart Seat Pods


Wheeled seats that move riders through the subway station. The seats securely dock into subway cars for safety during ride.

4. Bike Sharing

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Bicycles available for rental at any Suguo convenience store (common in China).

Bikes will be electronically tied to be part of CTN: location of each bike is mapped, and part of collision avoidance.

Vandalism is discouraged through a karma points system.


5. Child Bikes

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Also available through sharing platform for suburban riders further from subway system.

Folding bicycle that attaches to stroller.

Front portion of bike can be returned at station as rider continues journey with stroller.


6. Robo Valets

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Moving “drawers” for car storage underneath building eliminates hassle in looking for parking space and reduces CO2 emissions in enclosed area.

Vender machine-like nature makes car available at any entrance of building. Imagine if, after parking, you can enter a giant shopping mall from the East entrance and exit out the West and have your car waiting for you there.

7. Vehicle-to-human Empathy


Some traffic and crossing signals could be displayed on the sides of vehicles, aiding communication between pedestrian and cars.

In addition to self-driving capabilities, vehicle interior takes data of drivers’ emotional changes.

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The goal is to utilize existing modes of automatic transmission in higher end vehicles such as sport mode, fuel economy mode, and snow mode, and take it one step further; using real-time biometric data, the vehicle’s onboard computer can tell if a driver is emotionally distressed or angry. In those cases, the vehicle will adjust its transmission to accelerate more gently, or shift to a lower gear, thereby preventing the driver from speeding or driving erratically.


The knowledge and data that can be learned by the machine to improve performance over time include the reactions of the driver (from biometric readings) as well as the change in drivers’ habits. This biometric data may come from studies of human emotion, heat maps and cat scans, as well as research papers or even movies.


In order to perform the action. Onboard computer detects change in biometric data. Then it analyzes and interprets what happens and categorizes change in emotion (empathy)

Then, it interprets emotion intensity and probability of adverse effect on driving. The Computer detects biometric change and interprets it. It also alerts human of transmission adjustment or takeover. However, the human may choose to override this decision.


Compiled data about what biometric factors may constitute what sort of emotions. This biometric data can come from heart rate measure from the driver’s hands on the steering wheel as well as heat on hands and face. an infrared camera can be pointed at the user’s face from the front dashboard. that way, onboard computer can not only detect emotions, but health of the driver. In addition, i anticipate that there will be front facing cameras, as in the age of autonomous cars, there would probably be video calling. All the biometric data is quantitative. However, the whole is greater than the sum of its parts; while not much can be figured out from facial heat mapping and heart rate and blood pressure levels, these numbers together can represent different emotions. From that interpretation, the onboard computer can have “empathy”

Division of Labor

Subtasks of this task involves detecting change in condition, taking biometric data, analyzing and interpreting that data, and making a decision. They are mostly conducted by the onboard AI. However, human is given choice of overriding the AI’s decision.

8. AI Guided Smart Lanes

Choreographed Intersections Utilizing Autonomous Vehicles and Pedestrians

Choreographed Intersections Utilizing Autonomous Vehicles and Pedestrians


The goals of this smart system is to coordinate the vehicles of a city at intersections to move without collisions, resulting a choreographed “dance” of passing vehicles that do not stop, but merely pass without accidents. This performance is measured by the expediency of vehicles to their destination, accuracy of crossing, and overall efficiency of fuel economy on all vehicles.


To improve performance of this smart system over time, knowledge collected should involve collision probabilities of vehicles crossings as well as braking or changes in speed of vehicles. This knowledge can be learned through a variety of ways: through experimentation and testing through simulations that the AI runs through (it is best to do testing through simulations as testing actual vehicles and human lives is not ethical), and data from existing traffic apps such as Google Maps and Waze.


In order to perform this task, the traffic AI must analyze vectors from other vehicles that are probable to cross the next intersection. When these vehicles are near intersection, the traffic controller AI takes over the vehicle from the vehicle’s onboard AI, to control speed and steering. The vehicles the either speeds up or slows down so that with all other approaching vehicles, all may pass without hitting one another. After successful crossing, the traffic controller AI then returns control of the vehicle to onboard AI or human operator. The human operator is notified of approaching intersection. The traffic AI and vehicle onboard AI does the rest; however, this switching of tasks between the two machines are seamless.


Potential data sources that should be tapped for this may come from simulations and tests made during the testing stage, satellite imagery data, and vehicle GPS. It may also come from cell phone location data of nearby pedestrians, in order to keep their safety. In addition, data can come from car make and model, and furthermore, the benchmarks associated with car models. Despite the complexity of this type of problem, the useful information that comes from these data sources are presented in a way that is quite easy to interpret. Data from simulations and initial tests may show as organized data since they come from a controlled source. Sources such as longitude/latitude, location data, velocity, distance from intersection, etc, are all quantitative data that can be easily processed by a machine. Similarly, information about each vehicle such as make/model and year are readily available as are benchmarks for these vehicles such as fuel economy. However, data such as the amount of wear on the vehicle breaks may be more difficult to interpret. 

Division of Labor

These tasks can be decomposed into subtasks that  are to monitor intersections and plot vectors of moving vehicles and pedestrians, to decide which cars to speed up and which to slow down in order that they can pass without collision, and to find the optimal time of takeover from traffic AI to onboard AI. Within these processes, computers should be tasked with deciding speed and plotting collision avoidance. Humans should plot destination, and tell computer car behavior in intersection — are we going straight or turning? 

9. Energy Farming

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Electromagnets on the bottom of vehicles can have corresponding coils under the road, generating electricity. The slowdown that it may cause vehicles can be used productively: it can be implemented in downhill areas or school zones where a slowdown would actually be helpful.

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System adoption timeline
The timeline that the China Smart Metro can be adopted would be similar to that of a standard product lifecycle. However, in this case, “adoptors” would be the different cities in China that would implement it. The first adopters of the system would be small cities with large funds that come from tourism (ie, Suzhou). These cities can charge higher metro fares, and use the system on a smaller scale. After its proven success in smaller cities, the system can then be implemented in large metropolitans such as Beijing and Shanghai. Then, monetization opportunities can come from partnerships with businesses and tourism. When the smart metro becomes mainstream in China, it can then be adopted internationally, following the same pattern. Eventually, the Smart Metro can even be integrated with China’s landmarks to promote nature and National Parks

China Smart Metro: Process

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The China Smart Metro project began as an idea to design a baby stroller to ll the unique needs of urban parents in post-one child policy China.

However, through interviews and primary research, I discovered that their pain points may not be solved through a simple stroller, but through a redesign of the entire metro system.


The painpoints that parents with infants face on the metro were symptoms indicative of larger problems coming from society itself.

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Wheelchair users

Further research showed that it is dif cult for wheelchair users to move around inside of a metro station. Having elevators is not a complete solution: it is tiring for a disabled person to roll themselves a further distance, if they can even locate the elevator.

The Xiaomi wheelchair booster is detachable and allows wheelchair users a powered option without the need to switch to a different wheelchair.

Early iterations utilized a central hub attached to wheel with removable battery. However, it evolved to sit neatly atop the wheel for easier installation and better user experience.

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