Bicycles, and e-bikes in particular, are becoming increasingly popular in Germany. According to the Zweirad-Industrie-Verband e.V., the number of bicycles and e-bikes has grown by an impressive 11.8 million since 2012. That’s good for the climate. After all, switching to climate-friendly two-wheelers for commuting to work, school or for leisure reduces harmful emissions and promotes the transport transition. This is entirely in line with the Federal Government, which has even enshrined the promotion of cycling in the Climate Act. Unfortunately, the increased volume of cycling is also reflected in the accident statistics. According to Destatis, the proportion of cyclists among road fatalities has almost doubled since 2000.
But what can be done to prevent serious accidents?
One way is: Systematically collect and analyze information on minor accidents and dangerous situations and take measures at hotspots! According to the recognized theory of Hyden (safety pyramid), an increased occurrence of the aforementioned events indicates an increased risk of accidents. But how can this theory be applied in practice? Especially as many minor cycling accidents happen without the police being called. The Mobility Urban Safety Intelligent Cloud (MUSIC) collects data on these dangerous events on a daily basis. In addition to official accident data, important information from the public is received via the “gefahrenstellen.de” portal/app. Other data such as anonymized vehicle braking data is also included. The scientifically based hazard score methodology (FeGiS+) evaluates all data and displays it in a Germany-wide hazard map. In the SMART portal, data from municipal and official organizations can be analyzed for specific issues.
Analysis example for the early detection of critical cycling locations in SMART
The Institute for Road Engineering (ISAC) at RWTH Aachen University has applied the SMART early indicator analysis it developed to the city of Aachen. Places that are dangerous for cyclists were analyzed. The filter result was then further refined using the “Early indicator” button. This marks locations where accidents have so far been inconspicuous, but where there are high scores for user reports and vehicle braking.
The analysis brought the access road to an expressway into focus, where a bicycle and pedestrian crossing with right of way turned out to be critical. In addition to the user reports, the comparatively high number of sharp car braking maneuvers also pointed to conflicts. Users noted the poor visibility between crossing cyclists and motor vehicles. The excessive acceleration of the vehicles further exacerbated the danger situation at this crossing, which was confirmed by a three-week video recording by the ISAC. The camera recorded near-accidents in which the vehicle either braked sharply or narrowly missed the cyclist.
Conclusion
Of course, identifying a critical section of road at an early stage is only the first step towards greater safety. It is important to implement a suitable mitigation measure once the problem has been identified. In the example mentioned, short-term measures such as The greenery was cut to improve visibility between road users. An ISAC research project is currently investigating whether a new type of lighting system can be tested here in the medium term to encourage drivers to reduce their speed.
The automated SMART analyses provide important key figures for the daily traffic work of municipalities, authorities and engineering offices. By acting proactively, vulnerable road users such as cyclists and pedestrians can be better protected.