Dillon T. Fitch's research while affiliated with University of California, Davis and other places

... To address the limitations of big data on shared bikes regarding prior mobility information, some studies have integrated big data and small data in the assessment (Fishman et al., 2014;Reck et al., 2022;Fukushige et al., 2023). For example, based on travel surveys from five cities (Brisbane, Melbourne, London, Minnesota, and Washington D.C.), Fishman et al. (2014) calculate the percentage of car trips that were replaced by shared bikes. ...

... Relatively small populations characterize these areas; some are still under construction. The long idle duration means that the utilization rate of bicycle-sharing in this area is low, which may be due to three reasons: firstly, the area has a small population, slow economic development, and a lack of large shopping malls or residential areas in the surrounding area, so the use of bicyclesharing is low, resulting in a large number of bicycle-sharing being idle; secondly, the area is not easily accessible and cannot attract a large number of people; thirdly, the dispatching method of bicycle-sharing in the area is unreasonable, and when people need to ride they cannot find a bicycle-sharing nearby [35], thus reducing their willingness to ride [10]. According to the Xiamen Special Economic Zone Yearbook 2021 [36], the economic level of Siming District is higher than that of Huli District. ...

... With the growing popularity of shared micromobility as a new travel mode, planners are increasingly considering micromobility as a possible answer to reducing traffic congestion, reducing greenhouse gas emissions, and improving connectivity around cities (Wang et al. 2022). Recent studies in six cities in the United States have shown that dockless e-scooters and bikesharing are replacing 45% of trips that would have otherwise been taken in personal vehicles or ride-hail services (Abouelela et al. 2021;Basu and Ferreira 2021;NACTO 2020). ...

... The development of smaller, portable EDA recording devices has allowed for this method to be expanded in field research 11,[32][33][34][35] . Measuring physiological arousal with EDA is increasingly finding applications in pedestrian dynamics research ( 36-40 , see 41 for an overview). One of the first empirical studies on the "personal buffer comfort zone" using EDA was conducted by Engelniederhammer and colleagues 36 . ...

... Factors influencing riding experience can be derived from the rider-bicycle interaction [36]. In relation to the rider, riding style [37], riding skills [30,38], attitude [37,39], motivation [40] and sociodemographic characteristics [39,41] were identified as factors influencing the riding experience. In relation to the bicycle, riding dynamics affect the riding experience [30,42]. ...

... Several physiological markers of stress response are wellestablished and widely applied in controlled laboratory settings in psychology, physiology, and human factors and ergonomics (Antoun et al., 2017;Liu and Du, 2018). However, the validity of the stress markers for ambulatory studies is not yet established (Fitch, 2021;Smets et al., 2019;Wilhelm and Grossman, 2010), especially in environments like active travel, where body movement and other dynamic factors could confound the relationship between exposure to stimuli and physiological stress markers. Hence, further examination is needed of the validity of on-road stress measurements during active travel. ...

... Empirical studies investigating substitutive modal shifts provide evidence that the deployment of DLBS may attract travelers from PT and other modes of transport (Kong et al., 2020; such as cars, taxis, and ridesourcing (Bullock et al., 2017;Shaheen et al., 2013;Fishman et al., 2014;Barbour et al., 2019;Fukushige et al., 2021;Qin et al., 2018). However, the relationship between bike sharing systems and PT is multifaceted, encompassing not only a replacement model but also a cooperative model. ...

... In this case, shared micromobility would not reduce vehicle travel. Other studies have also demonstrated that shared micromobility has a negligible effect on vehicle travel (Fitch et al., 2021). ...

... Regarding social barriers and challenges, the potential e-bicycle users' characteristics, e.g., students, employees, the elderly, and their income levels should be taken into consideration, as these factors influence their willingness to use e-bicycles and pay for charging [45]. A communal e-bicycle system has the potential to significantly raise awareness about e-bicycles, although supplementary tactics may be required to transform this awareness into the active contemplation of using e-bicycles for daily commuting [46]. However, the heightened enthusiasm for increased speed and a riding style that diverges from the conventional cycling approach may significantly contribute to the involvement in precarious situations while operating e-bicycles [47]. ...

... Since the first bicycle route choice study based on revealed preference data was conducted (Menghini et al., 2010), a considerable number of studies have been published on this topic (and reviewed by Pritchard, 2018). In the last five years, the most popular areas to study the route choice preferences of cyclists were the Netherlands (Dane et al., 2019;Bernardi et al., 2018;Ton et al., 2017) and cities in North America (Shah and Cherry, 2021;Scott et al., 2021;Fitch and Handy, 2020;Sobhani et al., 2019;Khatri et al., 2016;Zimmermann et al., 2017;Chen et al., 2018;Park and Akar, 2019). Recently, researchers have been applying novel methodologies to reveal cyclists' behaviour, such as link-based models (Zimmermann et al., 2017), spatial models (Alattar et al., 2021) and machine learning techniques (Magnana et al., 2022). ...