# Mobility Patterns of Students: Evidence from Tricity Area, Poland
## 2 Literature Review
### Results  
Examining cohort-specific mobility patterns is a popular area of research. They are related to the general characteristics of lifestyle and vary over time, so usually, every ten years, new research results can be offered to verify the previous mobility trends for the particular generations. The lifestyle of people is also changing--they live longer, are healthier, better educated, and that is why they report demand for specific mobility services than the ones indicated for people over 50 years old and people younger than 30 years old [10]. [PERSON] [33] stated the mobility of young adults is still under-researched. [PERSON] and [PERSON] think the opposite--that there is an explosion for examining the millennial generation's travel behavior in the last few years [34]. The research toolkit used for this research is very rich and based on different fields of knowledge. In the defined literature, researchers used the theory of planned behavior [35; 36; 37], social cognitive theory [38; 39], energy cultures framework approach [11; 33], and socioecological models described by [PERSON] et al. [22] to explain the mobility choices of young adults. A popular method in the e-mobility area was life cycle assessment [40]. Most of the generational mobility studies were made with use of surveys [41; 42], interviews [33; 43], travel diaries [44] and observations [42; 45]. This way, the primary data can be obtained directly from the respondents and voices of different groups of customers become known. Especially interview is treated not as a stand-alone method, but usually supplementary to surveys or observations [46]. Lately, using the data from IT providers whose applications are installed on mobile devices [47] is a very popular source of data. Similarly, methods for data analysis can be divided into a few parts. The widest group of methods concern the multicriteria methods like best-worst method (BWM) [48], PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) [49], ELECTRE (Elimination Et Choin Traduisant la Realite) [50], DEMATEL (DEcision MAking Trial and Evaluation Laboratory) [51], AHP (Analytical Hierarchy Process), ANP (Analytic Network Process) [50], fuzzy methods [52] and many others. The methods other than multicriteria ones usually cover a large number of statistical analysis approaches like cluster analysis [53], nonparametric tests [54], ANOVA (Analysis of Variance) [55], factor analysis [56]. There is also a wide range of studies using qualitative methods [43; 57; 58]. Mobility patterns change over the life course. The number of vehicle kilometers traveled (VKT) is peaking at the age of 30-39 and is then decreasing. In general, women aged 18-30 are significantly more mobile than their men counterparts [59]. They are more likely to choose active transport modes and public transport [60]. In the older cohorts, the gender gap is still visible. Therefore, low mobility applies to young men. According to [PERSON] and [PERSON] [59], when women are in a relationship, their mobility decreases. On the other hand, an important factor in this aspect is raising children--women having at least one child are making more and shorter trips than non-mothers. Another direction of research is taking into consideration the matter of residential choices [61] because, in many societies, Y's choose dense urban areas [10; 20; 62] to live in and have access to many facilities they need [6; 16; 37; 63]. However, it also depends on their life course stage [62]--having children causes changing the residential choice for suburban areas (because of lower real estate prices). Special attention in examining the mobility patterns should be paid to the power of habits, including imitating family members and friends. In this field, habits were analyzed in the literature regarding mobility [64] as a whole and micromobility (e.g., cycling, skateboarding) [65]. [PERSON] et al. argued that habits are, together with social and personal norms, the most significant variables creating mobility patterns [66].