Abstract:

The relationship between income and travel behavior characteristics of urban residents is of great concern in urban geography. Income level of residents is an important indicator measuring regional social development, thus understanding this relationship is of great significance for city planning. Before the Big Data Age, due to the lack of residents' travel behavior information, it was difficult to study this relationship. However, along with the innovation of information technology, the use of ubiquitous sensors, such as mobile phones, has produced a large amount of human activity information, enabling the research on the relationship between residents' travel behaviors and income levels. In this study, based on the activity trajectory data in Shanghai Municipality from 27 December 2015 to 6 January 2016, we extracted a series of residents' mobility indicator data to measure mobility characteristics and conducted principal components analyses to extract the major components. We adopted the K-Means clustering method to classify residents into mobility groups and analyzed the feature of each group. Furthermore, the distribution of workplaces is shown to verify the difference in income levels between different mobility groups. Our results show that: (1) diversity of places to travel to and range of travel are two major components measuring residents' travel behavior; (2) residents who have smaller travel range and go to fewer places have higher average salary; (3) between the mobility groups, difference in income levels relate to industrial setup. These results may be useful for city planners to make efficient economic policies.

Key words: travel behavior, mobility indicator, income level, principal component analysis, K-Means clustering, Shanghai Municipality