Search

Bike facility features in urban transport systems are one of the most important elements for encouraging user choices regarding sustainable transport modes. The process of designing the bikeway does involve biker perception but the act of designing does not often rely on this perception. In order to identify whether gender differences exist for bike route choices, the actual choices made by bikers - both male and female - have been detected by means of GPS data, with the pathways characteristics being known. Detected route choices have been analyzed using the Oaxaca-Blinder decomposition method (Blinder, 1973 ; Oaxaca, 1973), which provides a possible explanation for differences in gender-specific route attributes that male and female cyclists experience under similar conditions. The results show that differences between female and male cyclists exist in terms of the ease of use of the pathways and related choices. Some analyses regarding age classes have also shown that gender differences tend to be less relevant with increasing age, thus suggesting that more-experienced female cyclists make choices similar to those of their male counterparts. • Identify gender differences for bike route choices based on the actual choices made by bikers under similar conditions. • Use the Oaxaca-Blinder decomposition method to explain the possible differences of gender-specific route attributes. • Assess if differences between female and male cyclists exist in terms of easiness of the pathways. • Understand whether gender differences tend to be less relevant with increasing age. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of characterizing cyclist typologies using a data set of GPS traces. The data is crowdsourced and consists of 29,431 traces recorded in the city of Bologna, Italy, during the rush hours from 7 am to 10 am on weekdays and during the months of April through September in 2017. The different criteria used to group the heterogenous behavior of cyclists into separate categories have already been described in literature and those studies were generally based on the stated preference in interviews or upon observing a small sample of the population. The novelty of this study lies in the clustering of a data set of 2135 cyclists that included GPS traces, which is equivalent to a revealed survey. Furthermore, refined pre-processing of the GPS traces allows the determination of dynamic attributes, a comparison of the chosen path with respect to the shortest path and the evaluation of other specific trip attributes, which are either difficult or impossible to assess by a classical interview. The applied clustering process leads to three main cyclist typologies, such that each type is characterized by different trip attributes and behaviors involving safety, riskiness, precaution, inexperience, knowledge, fear and hastiness: risky and hasty , sly and informed and inexperienced and inefficient. • Crowdsourced big data of GPS traces for the characterization of cyclist typologies. • Cluster analysis with more than 50 trip characterizing attributes. • Three cyclist typologies: Risky & Hasty, Inexperienced & Inefficient and Smart & Informed. [ABSTRACT FROM AUTHOR]