Abstract
Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.
Highlights
Cities are concentrated areas of human activities, and urban spatial structures are closely related to the intracity travel patterns of their residents [1]
With the rapid development of location-based service (LBS) and information communication technologies (ICT) such as Global Positioning System (GPS) receivers and mobile phones, these technologies support the convenient collection of large volumes of individual trajectory data [3,4,5]
A careful analysis of these digital footprints from taxi GPS data can provide an innovative strategy to improve the quality of public transit services and facilitate urban public transit planning and operational decision-making [23]
Summary
Cities are concentrated areas of human activities, and urban spatial structures are closely related to the intracity travel patterns of their residents [1]. A careful analysis of these digital footprints from taxi GPS data can provide an innovative strategy to improve the quality of public transit services and facilitate urban public transit planning and operational decision-making [23] This type of data has high accuracy and good continuity; they are acquired in real-time and support a high degree of automation. GPS taxi data can provide an excellent foundation for revealing urban residents’ travel behavior and analyzing spatial-temporal patterns [24,25]. A study by Yue et al [37] uses the dynamic taxi data to reveal people’s travel demands and movement patterns in a deeper sense to serve transport management, urban planning, as well as spatial-temporal tailored location search and services. The visual analysis of OD data is an important way to mine inter-regional flow patterns and analyze urban residents’ travel spatial-temporal patterns [41].
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