Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

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.

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

“…Using Chicago Metropolitan Agency for Planning (CMAP) data helped to compare and evaluate their model estimating OD travel matrix for non-community trips. In another study, Wang et al [41] worked with taxi GPS data to obtain travel patterns from OD flows. They proposed a chord diagram plot to illustrate the spatio-temporal patterns of residents in seven-day taxi trajectories in Beijing.…”

Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data

“…Urban mobility research studies the mobile flow of residents in urban areas [1][2][3][4][5][6]. The majority of human movement flows behave with a certain regularity, which are called normal urban mobility patterns.…”

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

“…For example, a spatial econometric model is widely used to explore the relationships between travel demand and a set of factors [16][17][18][19][20][21]. From the temporal perspective, applying time series models or dividing time into several periods is the most commonly used approach to understand the variations of travel demand at different times, such as weekdays and weekends, the morning peak, the evening peak and late at night [22,23].…”