Finding untapped cycling potential understanding traffic through merging IoT and traditional data

Purpose and goal:

Traditional traffic measures provide data that is difficult to understand and has limitations to identify cycling potential. The aim of the project was to try and develop new IoT based methods for data collection of qualitative and quantitative travel data, to better understand how, where and when people cycle, and why people are not cycling. The goal was to develop an analytical tool where the app based data easily can be analysed to identify untapped cycling potential which helps the city to reach its goals of sustainable travels. This was achieved.

Expected results and effects:

The IoT based methods gave better results than expected concerning representativity of traffic flows. The development of the analytical tool makes it possible to visualise whole trips, missing links, average speed on paths, purpose of trip, where short car trips is done, when and why the bicycle is not chosen etc. The results can now be used to prioritize measures and as input to the city’s new bicycle plan.

New knowledge on possibilities with IoT has been gained. The results has also shown potential for further development. Approach and implementation: The project was divided into four work packages:

1.Collection and compilation of the traditional and app based tavel data. The collection was mainly done during autumn 2021. The app contributed to data from 4000 trips. 2.Validation of the representativeness and reliability of the data

3.Develop the analytical tool TravelViewer to visualize the travel data. Make the data manageable and explore key functions.

4.Through the analytical tool, identify untapped cycling potential by examine how, when, where people cycle, and especially why trips that can be done by bicycle is done by car