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Speed and travel time of cyclists play important roles in the cyclist’s route choice and therefore there is a growing need on estimating the dynamic attributes of cyclists. Being able to quantify bicycle speeds on various facilities can help provide suitable accessibility measures based on estimates of travel time by bicycle and to calibrate and validate microsimulation models of cyclist’s behavior. Route choice and speed profiles may vary significantly among cyclists, depending on infrastructure characteristics as well as their personal characteristics (e.g., physical fitness and risk perception). The aim of this paper is to quantify how the personal and network attributes influence the cyclist’s speed, combining a big data sample of 270,000 GPS traces recorded in the city of Bologna, Italy, with a manual traffic survey. The novelty of the study regards the application to the data set of an algorithm that estimates travel times from map matched GPS traces and associates them with infrastructure attributes, after a successful validation of the data sample with manual observations and after testing its representativeness. The algorithm first estimates cyclist’s trip waiting times and those recorded on specific infrastructure elements from the GPS traces – which represents an innovation in the literature - and then obtains travel time as a difference with the trip duration. Results are sometimes different from those obtained in other studies, show a high correlation between the cyclist’s dynamic attributes and both cyclist’s typology and infrastructure attributes. The most interesting results are that average travel speed increases with road width, the number of lanes, road length and road priority. In the case study, average speeds on larger roads shared with motorized vehicles are even greater than those on separate level bikeways, which contradict previous studies. Male cyclists record on average an 11% higher speed than women, and faster cyclists have an age between 25 and 35 years old. Frequent cyclists are on average 5% faster than infrequent cyclists and cyclists even increase the average speed during rush hour of approximately 2%, without being affected by traffic congestion.

The increase of cyclist presence in urban areas and of the number of cyclist accidents on roads lead researchers to explore the in-traffic visual behavior and hazard perception of cyclists. In this study the actual cyclist gaze behavior while cycling on bicycle tracks—exclusive or shared with pedestrians is analyzed. The intent is to allow a better comprehension of those elements representing interferences, which can influence user’s trip. Field tests were performed in the urban center of Bologna, Italy. 16 participants were asked to wear mobile eye tracking glasses and cycle along a defined route. From gaze data recorded by the mobile eye detector, we analyzed which visual information are detected. By applying fixations detection algorithm and then a frame-by-frame analysis we calculated the proportion of fixations—number and duration-across different areas of interest. Proportion of fixations and fixation time are assumed as a proxy of visual workload. Thus, the relative frequency of fixation has been used to rank those elements that draw cyclist attention. Three are the main outcomes: first, an equilibrium of attention location between the central (trajectory) and lateral parts of the visual scene can be assumed as the optimal cycling visual condition. This condition results compromised when the presence of pedestrians is high. Second, discontinuities of the path (like intersections and crosswalks) and the presence of pedestrians are the elements requiring more attention. Third, the absence of physical and visual separation between cyclists and pedestrians seems to lead to a lack of attention to these risk elements. These outcomes about cyclists’ visual behavior allowed to recommend design measures to increase comfort and safety on shared-with pedestrian-cycling paths. Thus, suggestions are addressed in the conclusions.