OpenRouteService’s ‘Quiet Routing’ Creates a Less Stressful Route for Pedestrians

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Noise pollution is a growing problem in many urban environments, affecting citizens’ daily life. It can reduce citizens’ happiness, increase their stress, and even people them get sick if they are exposed to noise pollution for a long period of time. In recent studies GIScience Heidelberg investigates the use of crowdsourced data to derive noise polluted areas. Such information can also be used for generating routes that minimise exposure to noise.

Noise Pollution in the Urban Environment

Within urban environments, noise can be caused by many factors. Road traffic noise is one of the major noise sources. Besides, industrial areas, commercial places, and various buildings (such as chemical factories, power plants) also create noise. In the context of traffic noise emission, the GIScience Heidelberg team mainly use OpenStreetMap data to derive the noise levels, according to the category of streets. A noise level is roughly approximated for each street segment based on the relevant attributes of roads (type of streets, traffic lights, number of lanes, speed limit, etc.) and surroundings. For example, a main street usually has more traffic than a residential street and thus has larger noise values and affects larger areas (buffers). This is inspired by earlier work by Martinelli and allows to estimate noise polluted areas and to generate a comprehensive coverage, currently only based on OpenStreetMap data. At the moment tGIScience Heidelberg investigates the quality of this modelled proxy data by comparison to official noise data in order to calibrate and improve those very first approximations.

Introduction of the Experimental Routing Option: ‘Quiet Routing’

With the initially derived noise information, a new experimental prototype feature ‘Quiet Routing’ is integrated into the LABS.OpenRouteService.org for evaluation purposes. This is adding to the “healthy” (stress reducing) routing theme, that has been introduced recently starting with the “prefer green areas” routing option.

The new ‘quiet routing‘ feature in ORS can generate routes that avoid noisy areas. Moreover, an option of dynamically adjusting the weights is also added, allowing users to customize their routes and balancing between route distance and noise exposure. The same is now true for the “prefer green areas” option, that now also can be adapted individually by the user. For the sake of simplicity currently 10 levels are provided for each index. This leads to different weighting of the new attributes in comparison to the length of the route. So each user can decide individually how long a detour can be in comparison to the shortest route. Of course the results vary considerably with data quality and other factors what needs further ongoing investigations.

The feature also enhances the users’ awareness of noise along the route via the visualization of noise levels of route segments on the Web client. In addition to that these visualizations also help to understand the behaviour of the routing results according to the different weightings.

Currently the experimental prototype is available on the recently established LABS.openrouteservice.org platform together with other early features such as pedestrian routing through open spaces, the Places Location services API and others. These are availably initially only for all of Germany.

ORS Shortest Pedestrian Route without considering noise level

ORS Shortest Pedestrian Route without considering noise level

ORS QUIET Pedestrian Route considering noise.

ORS QUIET Pedestrian Route considering noise.

This work is inspired and supported through our cooperation project on urban stress (Psychogeography) with the Psychoepidemiologisches Zentrum (PEZ) at the Central Institute for Mental Health (ZI Mannheim) and the Intelligent Mobility Group at HeiGIT. The latter is kindly supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology). Follow up research can for example analyse the effects of the individual choices and preferences, also further attributes are under consideration. The HeiGIT team is looking forward to your feedback.

About the Author

Prof. Dr. Alexander Zipf is chair of GIScience (Geoinformatics) at Heidelberg University (Department of Geography) since late 2009. He is member of the Centre for Scientific Computing (IWR), the Heidelberg Center for Cultural Heritage and PI at the Heidelberg graduate school MathComp. He is also founding member of the Heidelberg Center for the Environment (HCE).

Since 2012 he is speaker of the graduate school “CrowdAnalyser – Spatio-temporal Analysis of User-generated Content“. He is also member of the editorial board of several further journals and organized a set of conferences and workshops. 2012-2015 he was regional editor of the ISI Journal Transactions in GIS (Wiley).

Before coming to Heidelberg he led the Chair of Cartography at Bonn University and earlier was Professor for Applied Computer Science and Geoinformatics at the University of Applied Sciences in Mainz, Germany. He has a background in Mathematics and Geography from Heidelberg University and finished his PHD at the European Media Laboratory EML in Heidelberg where he was the first PhD student. There he also conducted further research as a PostDoc for 3 years.



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