Planning a weekend with friends and family but you are not sure where to go because malls and public places are crowded. Therefore, real-time parking availability information will enable a smart choice before your move.

What it does

We combine machine learning, and information visualization to offer best and efficient service for all public end-users of MallNow application. Our solution will not only provide the end-user with the real-time information of parking availability but it will also forecast an estimate the level of crowdedness at the next hours of the day, and almost a week ahead, for all different Malls of a city. The end-user then can make a smart choice and/or plan.

How we built it

Team Configuration: UX Designer, Systems Analyst, Mobile Developer, Web Developer, Data Scientist

Performance stages: Idea Case Analysis, Drawing Process, Visualization of Screens, Establishing the Team, Development environment, start Implementing the Code and Integration, Verification and Validation.

Architecture Design: Here follows are the layers of the implemented solution along with the used technologies:

  • Presentation Layer: {Android Mobile App(JAVA), Admin Website(dotNEt)}
  • Service Layer: {Data Models(Entity Framework), Webservices/APIs(dotNet)}
  • Data Layer: {Database(SQL Server)}
  • Interface Layer: {Sensor(Simulated), Data Generator(Python)}

Challenges we ran into

Time management to ensure delivering the prototype on time and in an operational state.

Technical coordination between the available resources in the team, and task management.

A constraint-based design approach, especially when decided to simulate the Sensor data.

Accomplishments that we're proud of

We succeed to design the solution with features that fulfill the desired customer value.

We succeed to build our backend core system and a functional mobile app prototype.

We succeed to generate all the necessary input data by simulation (in lieu of Sensor).

We could show real-case results for our concept solution, due to achieved deliverables.

What we learned

Learning from the best; varieties of skills and knowledge areas within a team; and great hints from the mentors.

Breaking the routine of work and/or study, and start acting toward implementing innovative ideas and business.

Carefully considering every design decision; planning scope versus time-budget, and avoid any technical debts.

What's next for MallNow

The following stage will be the integration with actual parking systems from the different Malls and private parking service providers. We also plan to share some statistics with the local authorities and traffic department, to facilitate their planning to enhance the mobility within each city. More features will be incorporated in the next phase following data intelligence methodology, in order to improve the concept and expand the services along with investments options. One opportunity would be to add the capability of both reserved and free parking management (e.g. in advance parking assignment, and guidance to park on arrival) wherever the parking system and infrastructure allows.

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