THE REALIZATION

Mainz - Improve e-parking

Data

Mobility

Urban Planning

Mainz - Improve e-parking

The municipal utility in Mainz is testing the DKSR Open Urban Data Platform in the city area with the aim of establishing it area-wide if it proves successful. Parking violations at e-charging stations pose a challenge for the municipal utilities. These limit refueling options for e-mobilists and thus also the charging power turnover of Mainz's municipal utilities. By linking data from e-charging stations and parking sensors, utilization is obtained for a better understanding of the usage pattern of parked cars at charging stations. The analyzed data enables the derivation of strategies and concrete measures - e.g. providing e-mobilists with the occupancy status of charging pole parking spaces via the mobility app of Mainzer Mobilität.

Opportunities

Demonstration of synergies through the linking of different urban data sources in one platform.
Municipalities learning how an urban data platform can be used to add value to their services.
Deriving a taxonomy of charging behavior based on upstream analysis.
Making data-driven decisions withint the organization.
In the medium term, improve the availability of charging stations and increase sales of charging electricity.

Lessons Learned

In order to develop an evidence-based classification system of user behavior, it is necessary to conduct thorough testing and analyses of data samples(e.g., data from a charging station & parking sensor). In this way, the real-world usage patterns can be understood. Hence, a detailed data report was produced in advance.
Classification of e-charging behavior: Incorrectly parked (cars that park & do not charge), Time exceeded (cars that charge and exceed the maximum time), Correctly charged (cars that both park, charge, and leave the parking space before their time the allotted time has run out)
The documentation of the APIs must be well understood. It is often the case that they are not transparent and do not contain all the necessary information. Therefore, communication with company consultants is frequently necessary.
Deriving strategic measures: What insights can be derived from the data? For example, they could lead to a demand-based relocation of e-charging stations, to broader advertising, or to more flexible pricing for charging electricity.
In cross-organizational digitization and innovation projects, it is important to involve the management level from the outset and to make it clear that a prototype is being developed first and not a finished product or business model.

ADOPT REALIZATION

The source code of this implementation is available for free adaptation for members of the Urban Data Community. As a member, you can access the Urban Data Community's shared GitHub repository directly by clicking the button. If you have any trouble accessing the code, please contact us.