Resident Travel Characteristics Analysis Method Based on Multi-source Data Fusion

This study proposes an analysis method to investigate residents' travel characteristics based on multi-source data fusion by using traditional household travel survey and transportation big data. The residents travel characteristics were initially analyzed through a combination of traditional household survey data analysis (age, occupation, vehicle ownership, population distribution) and mobile phone signaling data analysis (travel frequency distributions). Then, the residents' travel characteristics were further analyzed through mobile phone signaling, Intelligent Card (IC), Automatic Fare Collection (AFC), the Global Positioning System(GPS) and other big data. The analysis results include residents' travel time distribution, Origin-Designation (OD) distribution, and travel mode structure. The resident travel characteristics of Guangzhou, China was analyzed as an example. The study then compared the proposed method with traditional household survey data analysis methods. The results indicated that about 30% residents' trips were not recognized by traditional household sampling surveys; the proportion of travel rate of twice a day generated by these two methods were 39.5% different; and the differences generated by these two methods for non-commuting trips, bus trips in PM peak hours, and subway trips in PM peak hours were respectively 7.4%, 8.1%and 12.6%. Compared with the traditional method, the multi-source data fusion analysis method is more effective to identify and analyze residents travel characteristics. It plays an important role to examine and balance residents' travel needs with the time and space distributions. Copyright © 2020 by Science Press.