A Comparative Analysis Based on Mobile Phone and Taxicab Usages

Exploring Human Movements in Singapore: A Comparative Analysis Based on Mobile Phone and Taxicab Usages ∗ Chaogui Kang Stanislav Sobolevsky Massachusetts Institute of Peking University Massachusetts Institute of Technology 5 Yiheyuan Road Technology 77 Massachusetts Avenue Beijing, P.R. China 77 Massachusetts Avenue Cambridge, MA, USA [email protected] Cambridge, MA, USA [email protected] [email protected] Yu Liu Carlo Ratti Peking University Massachusetts Institute of 5 Yiheyuan Road Technology Beijing, P.R. China 77 Massachusetts Avenue [email protected] Cambridge, MA, USA [email protected] ABSTRACT fied the divergences between observed human mobility pat- Existing studies extensively utilized taxicab trips and indi- terns based on taxicab and mobile phone data; (2) we imple- viduals' movements captured by mobile phone usages (re- mented an integrated approach of taxicab and mobile phone ferred as \mobile phone movements" hereafter) to under- usages for gaining more informative insights in population stand human mobility patterns in an area. However, all dynamics, transportation and urban configuration. these studies analyze taxicab trips and mobile phone movements separately. General Terms In this paper, we: (1) integrate mobile phone and taxicab Trajectory, Human Movements usages together to explore human movements in Singapore and reveal that mobile phone movements as a general proxy Keywords to all kinds of human mobility has substantially different characteristics compared to taxicab trips, which are one of Mobility Patterns, Taxicab, Call Detail Records the frequently used means of transportation; (2) investigate the ratio of taxicab trips and mobile phone movements be- 1. INTRODUCTION tween two arbitrary locations, which not only characterizes Understanding human mobility patterns has gain extraor- taxicab demands between these locations but also sheds light dinary attentions due to the rapid development of location on underlying land use patterns. acquisition technologies, complex network sciences and hu- In details, we quantify the distinct characteristics of mo- man dynamics. The hidden patterns of human movements bile phone movements and taxicab trips, and particularly in space and time are of great importance for traffic fore- confirm that the number of taxicab trips decays with dis- casting [9], tourism management [1], disease spread [22] and tance more slowly compared to mobile phone movements. evolution of social relationships [4]. Ongoing studies are From a spatial network perspective, taxicab trips largely re- also trying to unify a framework for smart city [17], ur- flect interactions between further-separating locations than ban computing [28], personalized recommendation [26] and mobile phone movements, resulting in emergence of larger transportation intelligence [12][10][6] by tracking individu- spatial communities (delineated based on people mobility) als' spatiotemporal activities. in Singapore. Revisiting the early age of human mobility studies, which The contribution of this research is two-fold: (1) we clari- is well known as Hägerstrand's (1970) time geography [8], researchers extensively utilized travel diaries to analyze in- ∗Please direct all correspondence to Chaogui Kang at dividuals' travel activities in space and time due to limited [email protected]. data collection ability. Fortunately, to date Information and Communication Technology (ICT) almost enables ubiqui- tous sensing of individuals' spatiotemporal movements in Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not real-time. Diverse tracking techniques have been adopted, made or distributed for profit or commercial advantage and that copies bear including circulation of bank notes [2], handheld GPS de- this notice and the full citation on the first page. Copyrights for components vices [27], Call Detailed Records (CDRs) [7], smart cards of this work owned by others than ACM must be honored. Abstracting with [13] and check-ins [3]. credit is permitted. To copy otherwise, or republish, to post on servers or to On the other hand, these distinct techniques capture sub- redistribute to lists, requires prior specific permission and/or a fee. Request stantially different mobility patterns, raising the challenge of permissions from [email protected]. UrbComp ’13, August 11-14, 2013, Chicago, Illinois, USA. activity interpretation and modeling. For instance, by track- Copyright c 2013 ACM 978-1-4503-2331-4/13/08 ...$15.00. ing the geographic circulation of bank notes in the United States, [2] inferred that human movements in space in gen- distribution (based on mobile phone usages) and taxi- eral follows the Lévyflight. However, those bank notes were cab usage in an area. carried by different people during the study period, mak- ing the inherent mechanism of human movements still un- The contribution of this research is two-fold: (1) we clari- clear. In 2008, [7] adopted the mobile phone data for mobil- fied the divergences between observed human mobility pat- ity analysis, and concluded that the (truncated) power-law terns based on taxicab and mobile phone data; (2) we pro- of collective human mobility emerged from a convolution of posed an integrated approach of taxicab and mobile phone population heterogeneity and individual Lévytrajectories. usages for gaining more informative insights in population Meanwhile, mobility patterns derived from taxicab trips are dynamics, transportation and urban configuration. better fitted by the exponential law than the Lévy flight The remainder of this article is organized as follows. Sec- model [11]. tion 2 describes the data preprocessing procedure for dis- Different techniques also bring ambiguity of the definition cretizing the study area and trip extraction. In Section 3, of human movements due to their inconsistent spatiotempo- we present the differences between taxicab trips and mobile ral data qualities. There exist at least three distinct strands: phone movements in terms of spatial pattern, distance de- trip-, shape- and sampling-based methods to define a single cay, and community structure. Section 4 provides a brief displacement. Taxicab trajectory is a typical illustration of discussion about potential directions of this research. In fi- the trip-based method in that the origin and destination nal, Section 5 summarizes the contribution of this paper and (OD) of individual movement is given explicitly. In hand- concludes the divergences between the mobility patterns de- held GPS trajectory, OD is unknown and thus applicable for rived from taxicab and mobile phone usages. the shape-based method. In this case, three different preprocessing methods, namely rectangular-, angle- and pause- 2. DATA PREPROCESSING based models, have been widely applied to extract trips [19]. Mobile phone positioning captures individual's discrete loca- 2.1 Data Description tions randomly in time as well as with low spatial resolution. In this research, we extracted GPS logs of 15; 000+ taxi- It thus follows the sampling-based framework. cabs (D1) and CDRs of 2; 000; 000+ mobile subscribers (D2) In general, divergences of existing observations of human in Singapore over a whole week, respectively. mobility patterns are attributed to four reasons: different In D1, taking February 28th as an example, the total transport modes (walking, bus, train, flight and etc.), dif- number of GPS points for all taxicabs is 17,653,329. Each ferent population groups (age, racial, occupation and etc.), point is associated with longitude, latitude, time, velocity, different geographical environments (road network, region, and status (occupied/ unoccupied) of the given taxicab. The geology and etc.), and different spatial scales (intra-urban, time intervals between consecutive GPS points vary along inter-urban and etc.). As known, transport tools play sub- with different taxicabs as well as time of the day, while 1 stantially distinct roles in transportation systems and in- second, 3 seconds and 5 seconds are the most common val- dividuals' travel behaviors. Data associated with different ues. transport modes generally depict human movements with In D2, the number of mobile subscribers accounts for ap- different purposes and at different spatial scales. People with proximate 40% of the total population in Singapore. For different social-economic backgrounds always utilize public each subscriber, the CDRs contain voice, SMS and data logs. transport modes and urban spaces distinctly. Geograph- Given the dense distribution of antennas in Singapore, it is ical environments such as land uses and accessibility also capable to positioning mobile subscribers in very fine spatial have remarkable influences upon human mobility patterns. resolution. The antennas routing the mobile phone usages Understanding constraints of aforementioned factors upon roughly construct the spatiotemporal trajectory of each sub- observed human mobility patterns is meaningful for human scriber. mobility modeling. In this article, we focus on exploring human movements 2.2 Discretizing The Study Area in Singapore using both mobile phone and taxicab data, and Technically, the precision of GPS log is usually higher target to clarify the distinct patterns derived as well as the than meters, while the precision of mobile positioning is underlying mechanism. Intuitively, mobile phone movement much lower. To make taxicab and mobile phone trajectories is a general proxy to all kinds of human mobility

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