Study on the characteristics of weekend trips to three types of non-work destinations based on multi-source data: a case study of Shanghai, China

Abstract The proportion of Shanghai residents' non-commuting trips in the total number of urban trips has been increasing year by year. Non-commuting trips, as an important symbol of modernized lifestyles, reflect the use of urban resources by people in production and life, and their dynamic distribution and changes have extremely important guiding roles in the city's fine-tuned management and resource allocation. Taking Shanghai as an example, this paper extracts three types of urban function grids, namely, urban park green space, commercial consumption and public service through the establishment of Shanghai urban grid function identification system based on multi-source data such as Baidu LBS (Location-BasedServices), data, POI (Point of Interest) data, etc. Then 3 types of non-commuting trips, namely, urban park, commercial and public service are further classified and identified, and their characteristics are analyzed. The results show that: ①Urban park travel frequency is low, and the proportion of long-distance trip is high. Zhujiajiao Town, Nanjing East Road Street and Lujiazui Street are three main travel gathering nodes in the city. There is a close relationship between the main urban area and the suburbs and the main urban area is more dependent on the suburbs. ②The travel frequency of commercial is the highest and the average travel distance is the shortest. The trend of trip agglomeration to the main urban area is significant and the trip distribution shows an obvious multi-center trend. ③The travel frequency of public service is high and the average travel distance is short. The trip distribution shows an obvious hierarchical structure, gathering in the main urban area and suburban new towns. ④Songjiang, Jiading and Qingpu in the suburban new towns have stronger attraction. The study suggests that non-commuting trips have an important impact on the urban spatial network structure, which needs to be further analyzed and compared with the spatial network structure formed by commuting trips to find the links and differences, so as to provide research support for the optimization of urban spatial structure.