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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. [ABSTRACT FROM AUTHOR]

In contemporary cities, the catering sector is much more than only food and/or eating. On the one hand, it is an important source of income, employment and waste; on the other hand, it provides many opportunities for eating out and social interaction. For a given city, where does the catering sector concentrate the most and how far are people, on average, from this location across hours of a day? Are people, on average, closer to the concentration around the prime dinner time? Taking advantage of open and big data, we geovisualised the answers in the context of Beijing, the capital of China, with a population of 22+ million people. Specifically, the data we used were the 2015 points of interest of Baidu Map (the Chinese counterpart of Google Maps) and 10–16 August 2015 cellular network data for Beijing. The former provide us with the number and location of most if not all restaurants around the local metro-served area (MSA) – that is, areas that are within an 800-metre radius of a metro station. The latter allow us to derive the average number of cellular phone users (as a proxy for the general population, residents and/or employees) by MSA across hours of a day. We depict the top five population/employment/residential centres (quantified by the average number of cellular phone users) at three different times of the day, and the top 10 food centres (measured by the number of restaurants) at the MSA level in Beijing. [ABSTRACT FROM AUTHOR]

Using Beijing as an example, this research demonstrates that smartcard data can be used to (a) assemble the required data for excess commuting studies, and (b) visualise related results. Based on both smartcard and household travel survey data, we find that the theoretical minimum commute is considerably lower for bus users than for car users in Beijing. This suggests that there is a greater inter-mixing of jobs–housing functions (i.e., a better jobs–housing balance) associated with users of that mode compared to the corresponding land-use arrangement for car users, who locate further from the central area (Tian’anmen) than bus users. The commuting range for car users is 9.4 km greater than for bus users. Excess commuting is slightly higher for bus users (69.5%) than for car users (68.8%). Commuting capacity values are slightly lower for car users than for bus users, implying that car users consume less of their available commuting resources overall than bus users, albeit only marginally. [ABSTRACT FROM AUTHOR]

• Redefine familiar stranger in big data era (FSiBE). • Empirically operationalize FSiBE and its probable determinants. • Identity determinants of FSiBE's count and odds among metro riders in Beijing. • Synthesize and discuss significance of FSiBE in cities. Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological and public-policy implications, which have only been sporadically mentioned and/or examined in existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on familiar strangers that are defined in the traditional manner based on "small data" such as survey responses. Then we reconceptualize "familiar strangers" against the backdrop of the emergence and increased availability of big and open data. Such familiar strangers are called "familiar strangers in the big data era" (FSiBDE). After this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro stations, it is spatial structure, population distribution, and transport network that significantly influence the count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy and planning implications for operating metro services and managing metro station. [ABSTRACT FROM AUTHOR]

This paper seeks to understand extreme public transit riders in Beijing using both traditional household surveys and emerging new data sources such as Smart Card Data (SCD). We focus on four types of extreme transit behaviors: public transit riders who (1) travel significantly earlier than average riders (‘early birds’); (2) ride in unusual late hours (‘night owls’); (3) commute in excessively long distance (‘tireless itinerants’); and (4) make significantly more trips per day (‘recurring itinerants’). SCD are used to identify the spatiotemporal patterns of these four extreme transit behaviors. In addition, household surveys are employed to supplement the socioeconomic background and tentatively profile extreme travelers. While the research findings are useful to guide urban governance and planning in Beijing, our methodology and procedures can be extended to understand travel patterns elsewhere. [ABSTRACT FROM AUTHOR]

Vibrancy is indispensable and beneficial for revitalization of historic districts. Hence, identifying built environment predictors for vibrancy is of great interest to urban practitioners and policy makers. However, it is challenging. On the one hand, there is no consensus in selection of appropriate proxy for vibrancy. On the other hand, the built environment is multidimensional, but limited studies examined its impacts on vibrancy from different dimensions simultaneously. The Baitasi Area is a typical historic district in Beijing, China. In this study, on the basis of a long-term repeatedly measured dataset generated from the Citygrid sensors, we investigated the spatiotemporal distribution of street vibrancy in Baitasi Area and examined its built environment predictors in two seasons (i.e., summer/autumn and winter), with pedestrian volume as the proxy for vibrancy and built environment portrayed from four different dimensions (i.e., morphology, configuration, function, and landscape). We found that (1) the street vibrancy in Baitasi Area is temporally relatively evenly distributed, but with higher spatial concentration; (2) microclimate and built environment are more significant in winter than in summer/autumn; (3) street morphology and configuration features are more significant predictors than street function and landscape features; (4) generally, streets with higher point of interest (POI) diversity, higher buildings, and stronger network connection tend to have higher vibrancy. This study provides decision makers with insights in revitalizing historic districts. • Street vibrancy in Baitasi Area is temporally evenly distributed and yet with higher spatial concentration. • Effects of built environment on street vibrancy is more significant in winter than in summer/autumn. • Comparatively, day-time street vibrancy is less affected by the built environment. • Street morphology and configuration features are more significant predictors than street function and landscape features. • Streets with higher POI diversity, higher buildings, and stronger network connection tend to have higher vibrancy. [ABSTRACT FROM AUTHOR]