ContAIn
Reference number | |
Coordinator | Stiftelsen Chalmers Industriteknik - Chalmers Industriteknik |
Funding from Vinnova | SEK 500 000 |
Project duration | April 2020 - June 2021 |
Status | Completed |
Venture | Transport Efficiency |
End-of-project report | 2019-05882eng.pdf (pdf, 388 kB) |
Important results from the project
The purpose of this feasibility study was to develop a concept for optimizing waste logistics, based on algorithms provided with data from IoT sensors that report the position and weight of waste containers. In a continued project, the goal is to further develop the concept into a product and eventually in time, to reduce the environmental impact of the Swedish waste industry. Some objective eligibility requirements analysis and to develop eligibility requirements for IoT sensors. However, it was not possible to develop algorithms for the concept within the framework of the project.
Expected long term effects
The next logical step is to build on the project´s results and create an effective tool to facilitate efficient planning of waste transports. The position sensors completed by TRAKK during the project meet the requirements for a central function here. The insights gained from the current situation analysis, as well as the important factors that affect how collection and disposal of waste containers take place, play an important role here. To move forward, a strategy must be developed to replace the weight sensors that were originally planned to be included in the system.
Approach and implementation
The project group consisted of 13 participants, of which Chalmers Industriteknik, TRAKK Telematics Solutions AB and Statens Väg- and the Transport Research Institute had the largest share. The cooperation between these has worked well. The main challenges in the project arose as a result of the original hypothesis of using cost-effective weight sensors not working in combination with the fact that the method of data collection later had to be changed due to circumstances in the field, which led to a situation of data collected by difficult to use for algorithm development.