One-sided or two-sided love? Visualizing connections between metro station pairs in Beijing

Abstract

As more and more metro lines and stations have started serving our metropolises, they have (re)shaped our travels and lives, creating (new) venues and (additional) opportunities for serendipitous contacts. To know the odds and locales of serendipitous contacts among millions of metro riders, we employ the smartcard data of the metro riders in Beijing to visualize metro riders’ identifical trip trajectories and to visualize/disclose connections between metro station pairs because of these trajectories. We find that the pairs that produce the largest and smallest numbers of identifical trip trajectories are not randomly distributed in the city. Rather, they concentrate in the northwest and in the central, respectively.

Keywords

Serendipitous contacts smartcard data metro odds locale Beijing

In cities, public spaces could contribute substantially to diverse social interactions, including serendipitous contacts (Jacobs, 2006). Serendipitous contacts occur more frequently among strangers than among acquaintances. Such contacts that are due to close physical proximity among people are referred to as one kind of co-presences (Zhao, 2003). Physical co-presences (PCPs) are a critical element of booming economy and healthy communities (Gehl, 2011; Storper and Venables, 2004). Recurrent PCPs increase when people share similar or the same mode(s) of travel and trip trajectory(ies). PCPs first lead to familiar strangers, who encounter one another regularly. Familiar strangers then produce acquaintances and even friends under new conditions (Milgram, 1972). To many people, their acquaintances, friends and partners were initially familiar strangers. However, we still know little about the odds and locales of both serendipitous contacts and familiar strangers.

As more and more metro lines and stations have started serving our metropolises, they have (re)shaped our travels and lives, creating (new) venues and (additional) opportunities for serendipitous contacts and familiar strangers (Zhou et al., 2018a, 2018b). In this paper, we strive to use the smartcard data from Beijing to visualize the spatial patterns of serendipitous contacts between metro stations. The data is for August 10 and 14, 2015. One-way journeys (OWJ) is a trip between two distinct stations A and B. Round-trip journeys (RTJ) is one trip from A to B plus the other from B to A on the same day. OWJ and RTJ provide new lenses for us to look into the connections between different station pairs.

Based on the above concepts, we categorized probable serendipitous contacts among metro riders into one-sided lovers (OSLs) and two-sided lovers (TSLs). OSLs are riders who share the same OWJ (from A to B) at least twice in the five weekdays. OSLs represent some OSL relationship between a pair of stations. The more OSLs the stronger the OSL relationship. TSLs are a subset of OSLs, who completed at least two RTJs between A and B. TSLs constitute the TSL relationship between A and B. For any station pair, we assume that the more TSLs, the more serendipitous contacts and the more harmonious the relationships.

Figure 1 visualizes the connections between metro station pairs in Beijing based on the OSL and TSL concepts. The darker the color, the more TSLs there are. The width of the line reflects the percentage of TSLs, which equals to TSLs divided by OSLs. The wider the line, the higher percentage of TSLs. For the whole city, the percentage is between 22% and 50%. A lower percentage represents fewer serendipitous contacts and a less harmonious relationship between a station pair.

Figure 1.

The number and percentage of two-sided love builders.

Interestingly, the station pairs with a larger number of TSLs also have a higher percentage of them. The station pairs with the largest number of TSLs are not randomly distributed in the city. They tend to concentrate in the northwest, where there are (a) many leading high-tech companies’ headquarters, national research and development institutions and top universities in China; and (b) some of the best public primary, middle and high schools in the city. Similarly, the station pairs with the smallest number of TSLs are also not randomly distributed. Somewhat surprisingly, the most visible of them concentrate in the center of the city. In other words, many station pairs therein do not “love” one another: they produce fewer RTJs than their counterparts elsewhere.

In summary, the concepts of OSL and TSL and smartcard data have enabled us to identify and quantify “love” between station pairs. The Beijing case indicates that the strongest and the weakest loves might concentrate in certain locales. We still know very little about what contributes to this, but it is intriguing for us to explore more. The resulting knowledge would not only help us to understand better serendipitous contacts, but also how to better utilize the carrying capacity of metro services between different stations.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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