This study presents a list of invasive alien plants that are found along roadsides in seven selected European countries – Austria, Germany, the Netherlands, Ireland, Norway, Slovenia and Sweden – and an overview on the role of roadsides as a habitat for invasive alien plants. This compilation is based on national lists of invasive alien plants, a literature search and expert consultation. Comprising 89 species from 31 plant families, species introduced for horticulture dominate the list (65%). Thirteen species (14%) are widespread (occur in four or more countries) and include well‐known invasive plants such as Fallopia japonica, Heracleum mantegazzianum, Solidago canadensis and Solidago gigantea. Seventeen species are listed either on the EPPO List of Invasive Alien Plants or on the EPPO A2 List of pests recommended for regulation as quarantine pests. Five species are on the List of Invasive Alien Species of Union Concern (EU Regulation 1143/2014). The compiled list provides a snap‐shot of invasive alien plants currently found along roadsides in the selected countries. It allows for a more targeted approach to monitoring, containment and control of the most problematic invasive alien plants identified in each country. Moreover, the list may also be used to identify emerging (potentially) invasive alien plants along roadsides in other European countries that warrant monitoring and/or management.

Departing from the EC-funded project WEATHER, this paper delves into the subject of adaptation strategies by revisiting the project's general findings on adaptation strategies and by adding two specific cases: (1) advanced winter maintenance on roads in southwest Germany and (2) technical and organizational measures in Alpine rail transport. For these two cases, feasible adaptation strategies are elaborated and their potential is discussed in light of damage cost forecasts up to 2050. For the road sector, we find a high potential to mitigate weather-related costs, although damages here are expected to decline. In contrast, rail systems face strongly increasing damages and the mitigation options offered by improved information and communication systems seem to be largely exploited. Consequently, it is easier to justify expensive adaptation measures for high-cost rail infrastructures than for road transport. A generic analysis of 14 damage cases worldwide, however, 123Nat Hazards DOI 10.1007/s11069-013-0969-3 revealed that generally awareness raising, cooperation and communication strategies are sufficient to mitigate the most severe damages by natural disasters.

Sound exposure data are central for any intervention study. In the case of utilitarian mobility, where studies cannot be conducted in controlled environments, exposure data are commonly self‐reported. For short‐term intervention studies, wearable devices with location sensors are increasingly employed. We aimed to combine self‐reported and technically sensed mobility data, in order to provide more accurate and reliable exposure data for GISMO, a long‐term intervention study. Through spatio‐temporal data matching procedures, we are able to determine the amount of mobility for all modes at the best possible accuracy level. Self‐reported data deviate ±10% from the corrected reference. Derived modal split statistics prove high compliance to the respective recommendations for the control group (CG) and the two intervention groups (IG‐PT, IG‐C). About 73.7% of total mileage was travelled by car in CG. This share was 10.3% (IG‐PT) and 9.7% (IG‐C), respectively, in the intervention groups. Commuting distances were comparable in CG and IG, but annual mean travel times differ between x¯  = 8,458 min (σ = 6,427 min) for IG‐PT, x¯  = 8,444 min (σ = 5,961 min) for IG‐C, and x¯  = 5,223 min (σ = 5,463 min) for CG. Seasonal variabilities of modal split statistics were observable. However, in IG‐PT and IG‐C no shift toward the car occurred during winter months. Although no perfect single‐method solution for acquiring exposure data in mobility‐related, naturalistic intervention studies exists, we achieved substantially improved results by combining two data sources, based on spatio‐temporal matching procedures.

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