Tunnll is an urban bus service for small towns with limited or no budget.
Mass public transportation is an expensive operation. It needs to keep running many buses, even when some buses are empty. Hundreds of small towns around the world cannot afford that. The waiting times in those towns are very long when there is any public transportation available at all.
Non-existing public transportation leads to ever-growing towns’ dependence on personal cars which are one of the main contributors of greenhouse gas emissions, air pollution and traffic congestion. The inefficient bus usage results in more than 1 billion people (specifically low-income residents) having limited social mobility. People leave small towns in favor of bigger cities which worsens the de-population trend.
Tunnll is a next-generation mass transportation system for small towns. It is more efficient than fixed timetable bus services and than traditional Demand-Responsive Transport. Thanks to its efficiency, Tunnll is a sustainable solution for small towns with limited or no budgets.
The main objective of the proposed project is to advance the existing pilot (private beta) to a full-scaled system and validate its ability to serve a massive number of daily users.
As a cyber-physical system, Tunnll continuously accumulates Big Data from passengers and buses. It then applies advanced geospatial and resources allocation algorithms to establish new dynamic meeting points.
Tunnll enables access to key destinations and services within a small town, regardless of the level of income of any individual passenger. In a Tunnll-enabled town, any citizen can afford to access any of the amenities available in town. As long as a low-income individual can afford to pay for a daily journey to work, university, school, clinic, or any other place in the town, Tunnll’s social impact is validated. This is a life-changing technology for many.
The Key Exploitable Result of this experiment is an intelligent urban transport system that is:
- Ready for use (because it has proven its ability to withstand the daily load);
- More efficient than a timetable-based public bus transport service in terms of passengers per vehicle per hour;
- Can attract more passengers thanks to a significantly better User Experience.
KPI 1: Fault tolerance.
KPI 2: Accepting orders under high load.
KPI 3: Processing orders under high load.