Overtaking in Stuttgart: lateral distance between vehicles analysis

In the context of climate change, it is desirable to increase the share of cycling. One way of doing this is to strengthen subjective safety of cyclists. At present, many people perceive cycling as unsafe. In particular, overtaking by motor vehicles is a cause of low subjective safety and stress. In built-up areas, German road traffic regulations stipulate a minimum lateral distance of 1.50 m for motor vehicles while overtaking cyclists. Previous research has shown that this rule is often not followed by motor vehicles. The aim of this study is to find out which factors influence the lateral distance of overtaking manoeuvres. The lateral distances of 4,081 overtaking manoeuvres were recorded using an ultrasonic sensor on 14 selected routes in the city of Stuttgart, Germany. Forty-two percent of the recorded overtaking manoeuvres were carried out with a lateral distance of less than 1.50 m. The mean value of all overtaking manoeuvres was 1.59 m. On roads with mixed traffic, higher lateral distances occurred than on roads with cycle lanes. In Germany, the motor vehicle traffic volume on a road is a key criterion for planning cycling infrastructure. However, it is not possible to confirm an influence of the motor vehicle traffic volume on the occurring lateral distances. The time of day at which overtaking manoeuvres take place also seems to have no effect on lateral distances. 

Supporting evidence

The article has three main variables that were taken into account: 

The effects of:

• cycling infrastructure,
•  motor vehicle traffic volume, and
•  time of the day on the lateral distances between motor vehicles and cyclists. 

The data was collected on trips undertaken specifically for this purpose, in the City of Stuttgart with the author of this paper being the only test person. This makes it possible to collect data on systematically selected streets. Also, due to a consistent riding style, comparability of the data among each other is ensured. 

The data was collected using a device called OpenBikeSensor to measure lateral distances using ultrasound. 

In all, a total of around 790 km were covered and 4,081 OMs were recorded on 14 measurement routes. On all routes, cycling traffic is guided either in mixed traffic or on cycle lanes. German guidelines distinguish between two types of cycle lanes. Cycle lanes with dotted lines are not mandatory for cyclists and may be used by MVs under certain circumstances. Their regular width is 1.50 m and their minimum width is 1.25 m. Dotted cycle lanes are considered to be part of the carriageway. Cyclists must use cycle lanes with solid lines and may not be used by MVs. Their minimum width is 1.85 m. Solid cycle lanes are not part of the carriageway, but rather a separate path.

Key findings

Data Collection Strategies 

Establishing clear definitions is a priority to ensure consistency across studies. Researchers should define fundamental concepts such as “pedestrian” and “cyclist,” referencing Eurostat’s Passenger Mobility Statistics (2015). Using harmonized definitions across datasets, including Eurobarometers and EHIS, ensures better cross-study comparability. Clarifying the term “urban population” is critical, as inconsistent interpretations emerged from survey responses. Adopting frameworks like the degree of urbanisation (Urban Audit or OECD-EC) will align sampling strategies and improve the analysis of active mode use in relation to socio-economic indicators. 

A consistent definition of “trip” is essential for reliable statistics. Researchers must specify how trips are recorded—whether focusing on the main mode or multiple modes—and define boundaries (core city vs. metropolitan area). This clarity ensures comparability of metrics like average trips per person and daily distance traveled. For infrastructure, researchers can utilize the European Cycling Lexicon but should expand it to include 30 km/h zones, aligning with sustainable urban mobility indicators. For walking infrastructure, it is recommended to assess pedestrian-friendliness through qualitative indicators rather than rigid definitions. Employing street sampling and standardized audit protocols can complement crowdsourced data collection for infrastructure quality assessment. 

Data Management and Analysis 

Post-processing techniques are vital to generate comparable statistics on daily travel behaviors. Researchers must consider seasonal and regional variations, as mobility patterns differ between regions such as Scandinavia and the Mediterranean. Incorporating active mode data into surveys like the Quality of Life in European Cities will provide consistent datasets for longitudinal and cross-city studies.

Systematic monitoring of active modes should be integrated into the OECD’s Road Safety Annual Report. Researchers are encouraged to explore crowdsourcing platforms, such as OpenStreetMap, by refining mapping rules and promoting public participation. Future research should investigate big data tools, like Google Better Cities, to mitigate biases and generate comprehensive pedestrian datasets.