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Innovation Design Engineering (MA/MSC)

Alessia Zhang

Alessia Zhang is a hybrid designer leveraging her sensibility and curiosity to explore the world.

Her experimental approach deeply investigated the relationship between object, human and the environment, leading her design works ranging from education, ecology and sustainability.

After receiving her BSc in Industrial Design at Politecnico of Milan, Alessia has worked in international companies such as Frog and Microsoft, where she gained the ability to work in an interdisciplinary team and make a real impact through the product in physical or digital form. She is also a freelance visual designer, photographer and social innovator active in Europe and Asia.

The negative environmental impacts caused by transportation are the core issues of sustainability. Globally, it accounts for around a quarter of CO2 emissions. On the other hand, our travel behaviour is one of the most entrenched behaviour. People are tempted to act in their personal interests to meet their own travel demands despite the impact on the environment, making transportation a battlefield of value conflicts between environmental needs and human's self-interest. This project is trying to tackle the wicked problem: 

How can we transform the conflict to create opportunities for mutual benefit? 

YOYWAY is a net-zero transportation circular model optimized by streaming media. It presents a mobile app for travellers, backed by an open collaboration platform for streaming providers.

I believe that environmental, experiential and economic benefits are not mutually exclusive. YOYWAY uses the design to meet human need, creates a business model to align with commercial imperatives and creates value to benefit the environment, allowing data science to act as a bridge to allow nature and humans to communicate effectively, without threat or temptation.

Travel Time Perception Model
Travel Time Perception Model

Time is definitely the most critical determinant in travel mode decision making. At face value, time savings is the measurement of a human's time perception. But the truth is that there is another dimension - time quality, together with time savings to influence human's time perception in an integrated way. What if we offer an extra intervention to adjust traveller's streaming experience and build a positive correlation between the time quality and sustainability´╝č


Yoyway is a low-carbon transport circular model optimized by streaming media. It presents a mobile app for travellers to complement their streaming experience, backed by an open collaboration platform for streaming providers.

The strategy is used to increase users' susceptibility to persuasion and provide targeted intervention to suit individual needs. It makes the needs of sustainability hidden underneath the user's needs and encourages the users to shift their behaviour pattern to make a certain environmental impact.

First Download
Trip Planning_1
Trip Planning_2
Depart/ On the Way/ Switch Vehicle
Non-travel Time
Non-travel Time_2

In the current system of stakeholders, there is no intersection between the traveller and the streaming provider

To reduce the carbon from the atmosphere, the travellers have a more direct and effective approach but vulnerable motivation. The motivation of streaming providers is more strongly linked to their commercial interests, but the effectiveness of carbon offsetting as a method of carbon reduction still remains controversial. 

With YOYWAY as the medium, the whole system becomes an interrelated and dynamic circular model. 

With the special access to massive streaming resources, we can provide users limited-time access to resources that would otherwise not be available or freely available. We provide a soft but persuasive intervention with various configurations of the media resource.

The SSP provides us with resources as their contribution to sustainability. YOYWAY relocates the resource and generates personalized interventions that encourage users to adopt sustainable transportation habits.  

How it works

In this system, data science plays a crucial role as a core engine. Specific algorithms of data collection and processing are used to calculate the values of sustainable transport to ensure the fairness of conversion. The system consists of a GPS mobility module and a recommendation module.

Machine learning algorithms are applied to process GPS for travel mode detection and moving distance tracking to leverage mobile sensing to uncover users' travel patterns. Transport modes are labelled as walk, bike, transit, driving, and train. Besides that, a recommendation system is used to construct users' persuadable playlist by considering their interests and cravings.