Undoubtedly, the advancement of technologies in Tallinn and Helsinki has greatly contributed to making travel between Estonia and Finland more quantifiable. Currently, both cities are striving to establish collaborative data models in the transportation sector to enhance the quality of travel for passengers, i.e., combining public transport ticket systems . The IoT-NGIN Smart city use case is exploring prospects for data sharing between the cities, which enables the measurement of traffic congestions and crowdedness, and facilitates ride-sharing options based on the data from social networks.
However, what might be the possible future ideas for smoother travel based on the result of IoT-NGIN’s Smart city LL?
The IoT-NGIN’s Smart city LL has three use-cases that aim to enhance urban mobility through innovative solutions and open data models, with a particular focus on cross-border collaboration between Helsinki and Tallinn using the Finest Twin Cities platform , used for data capturing and processing. An example of such a collaboration area is the busy Helsinki-Tallinn commuter harbor.
The IoT-NGIN’s Smart city LL cross-border collaboration between Helsinki and Tallinn highlights the necessity of expanding data-sharing efforts. With a deeper focus on ferry organizations’ service offerings, there is a clear opportunity to improve the traveler experience by sharing more data related to lodging, dining, entertainment, travel plans, and more, resulting in the further development of Smart city use-cases that are production-grade and traveler-friendly. At least two logical interconnected ideas can be evolved from the IoT-NGINs Smart city LL to further be implemented by ferry organizations:
Carpooling extension: The ferry organizations could create a digital extension to the existing ticketing system where passengers can connect with each other to share rides from the harbor to their final destinations. The extension could use data from the FinEst platform, such as real-time traffic information, congestion data, weather data, etc. to optimize ride-sharing routes and minimize travel times.
The technical package of IoT-NGIN’s Smart city LL strongly complements the carpooling idea by providing several inputs. They are:
- By using traffic flow prediction models from the Traffic flow prediction use-case, the carpooling system can suggest optimal pickup and drop-off locations to minimize the travel time for all passengers.
- The carpooling extension can benefit from a crowd management use-case by predicting the number of people who will be traveling between Tallinn and Helsinki during peak hours. This information can be used to suggest alternative routes or travel times to avoid congested areas, reducing the travel time for carpoolers.
- The carpooling extension can use citizen-generated data from social networks to match potential carpoolers based on their location, destination, and travel time preferences. This can help increase the number of people willing to carpool, making the extension more effective and reducing traffic congestion.
Further integration of carpooling extension into MaaS solution: The ferry organization could expand its services to offer more integrated travel options, such as public transportation, bike-sharing, and taxi services, as part of a MaaS integration. As part of potential cooperation, the MaaS provider could provide an API that allows ferry organization to access their booking system and offer public transport, taxi, and bike services as part of their service offering. This would enable travelers using a ferry to seamlessly book and pay for their transportation tickets through the ferry organization’s ticket system.
In terms of privacy concerns, such ideas should adhere to data privacy laws and regulations. The data collected, for example by the data capturing component, is supposed to be anonymized and used solely for improving urban mobility and enhancing the travel experience of the public. To be more precise, such data might include noise data, weather data, street-level data, etc. The system does not and will not collect any personally identifiable information, ensuring the privacy and security of the end user’s data.
In conclusion, the IoT-NGIN project and its Smart city use cases have demonstrated the potential for technical collaboration between Tallinn and Helsinki in the domain of traveler experience. With the abovementioned ideas and initiatives, the twin cities can continue to develop and leverage their strengths, creating a more seamless and enjoyable experience for travelers between Helsinki and Tallinn.
 FinEst Twins