Traffic volume is the main independent variable of risk exposure in road safety models. Cyclists as a vulnerable road users are more exposed to weather conditions than e.g. car drivers. As a result, their decision of whether to cycle is strongly related to weather conditions. It suggests that any change in the weather may have a significant effect on bicycle use. Objective of the paper was to indicate which weather parameters have a significant impact on bicycle use, how a change in weather parameters affects the change in bicycle volume (risk exposure) and, consequently, predicted number of crashes with cyclists and which factors differentiate the impact of weather conditions on bicycle volume. The impact of weather on bicycle volume variability was estimated based on literature review. The Web of Science, Scopus and TRID databases were searched. Finally, 33 papers from 1977 up to 2020, different in terms of the methodology used, country of origin, and analyzed group of cyclists, were reviewed. The impact of change in weather conditions on the predicted number of crashes with cyclists was estimated using own road safety models and previous research results. Results indicate that air temperature, precipitation, sunshine, cloud cover, humidity, and wind strength, have a significant influence on bicycle use. The impact of the weather on bicycle volume differs between different cyclists’ groups (different levels of experience, age, gender), trip motivations (recreational, commuting, etc.) and locations (countries, cities, climate zones). The paper shows complexity of impact of weather conditions on cycling and sensitivity of relationship between weather conditions and bicycle volume (i.e. risk exposure) and, as a consequence, bicycle safety. Results indicate that weather conditions should always be taken into consideration when analyzing cycling, especially in road safety analysis. The discussion of presented research results, research methods used with their limitations, and recommendations for future research were described.

Road crossings across two or more lanes in one direction are particularly dangerous due to limited sight distance and high vehicle speeds. To improve their safety, road authorities should provide safety treatments. These may include additional measures to reduce speed and narrow the road cross-section and the introduction of active pedestrian crossings. Equipped with flashing lights activated automatically when a pedestrian is detected, the crossings are painted red and have an anti-skid surface on approaches. The article presents an analysis of road user behaviour at pedestrian crossings on dual carriageways with a varying provision of road safety measures in some Polish cities. It also evaluates the effectiveness of the measures over time. The study was conducted before, immediately after and one year after the additional signage was introduced. The evaluation is based on how vehicle speeds changed before the pedestrian crossing, how pedestrians behaved versus the vehicle and their readiness to cross the street. The number of conflicts on selected crossings was also evaluated. The safety treatments under analysis were found to be less effective than the traditional pedestrian safety measures such as speed cushions or roads narrowed to one lane. This suggests that if used on dual carriageways the measures should only be temporary and should ultimately be replaced with traffic lights or a grade separated solution (a footbridge or tunnel) on exits from urban areas. No clear-cut conclusions about pedestrian safety can be drawn based on the traffic conflicts in question. The article is divided into the following sections: introduction with a review of the literature on pedestrian and driver behaviour studies at pedestrian crossings, including midblock crossings and dual carriageways; a description of the research method and test sites, the results, discussion of the results and conclusion.

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

The increasing number of cyclists in cities around the world results in a greater focus on bicycle traffic. Next to traffic volume, the main characteristic of traffic used in road safety analysis, infrastructure planning, design, etc. is its speed. Bicycle speed is strongly affected by the type of bicycle facility, motor vehicle traffic parameters (volume, speed, share of heavy vehicles), trip motivation, weather conditions, etc., and therefore it is difficult to estimate. Traditionally, bicycle speed is determined directly using speed radar or indirectly, as a quotient of measurement base length and travel time calculated using a stopwatch or video technique. There are also researches where bicycle speed was estimated based on GPS sources, mainly mobile apps. However, depending on the GPS source and the group of cyclists, bicycle speed gained from GPS data can be different from the speed of regular cyclists (due to different levels of experience or types of bicycle). In the paper, the relationships between bicycle speed obtained from empirical measurements and two different GPS sources, which were bikesharing system (Wavelo) and Strava app, were analysed. In total 18 research sites were selected different in terms of bicycle facility (bicycle path, shared pedestrian/bicycle path, contraflow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two-tailed test for two means was conducted to analyse the statistical significance of differences in bicycle speed estimated based on GPS data and empirical measurements using video technique. It showed that Wavelo and Strava speeds are by 17.4% lower are by 23.1% higher than the speeds of regular cyclists respectively. Two linear regression models describing relationships between bicycle speeds from empirical measurements and GPS data were developed. The results show that the variance of bicycle speed is almost 80% described by the variance of Wavelo speed and 60% described by the variance of Strava speed, which suggests that bicycle free-flow speed can be estimated based on GPS data either from bikeshare system or dedicated app.

Conventional road safety analyses based on police-reported crashes are limited by long observation periods, small sample sizes, and substantial underreporting, and may not adequately capture how built environment features (BEFs) and points of interest (POIs) influence safety. Therefore, surrogate safety measures (SSMs) have been increasingly investigated as complementary indicators of safety issues. This study evaluates whether GPS-derived SSM and police-reported crashes provide consistent evidence for assessing cyclist safety and identifying hazardous urban environments. The analysis integrates three years of police-reported crash data (2016–2018) with SSM based on bicycle deceleration rates derived from GPS data collected during 150,000 bikesharing trips recorded in June 2017. BEFs and POIs are identified using OpenStreetMap and Carto databases. Relationships between safety outcomes and urban features are evaluated using two-proportion Z-test with a Hazard Factor (HF) and a binary logistic regression model estimating Odds Ratios (OR). To examine the influence of spatial scale, four buffer sizes are applied. The results indicate that roundabouts, parking areas, pedestrian/bicycle crossings, bus and tram facilities, shops, leisure infrastructure, hotels and universities are associated with a statistically significant negative effect on bicycle safety (p-value ≤ 0.05). Comparison of crash and SSM data revealed that pedestrian–bicycle paths, sidewalks where cycling is permitted, and some public transport facilities present substantially higher frequencies of SSM events than suggested by crash records, revealing potential underrepresentation in official statistics. The consistency between HF and OR estimates demonstrates that proportion-based methods can provide results comparable to more complex regression models while remaining easier to apply and interpret. Variations across buffer sizes underscore the importance of scale-sensitive spatial analysis when assessing the safety implications of urban infrastructure. Overall, the findings demonstrate that SSMs with crash data provides a more comprehensive understanding of bicycle safety and helps identify hazardous locations that may remain undetected in conventional crash analyses.