The booming bike-sharing business provides great convenience to people's daily travel and brings notable change to city traffic. Meanwhile, few studies have analyzed the basis patterns of sharing-bikes and their influence on the traffic using empirical data. In this paper, we investigate the data provided by one of the leading bike-sharing companies. The city is gridded into regular rectangle regions using a geohash algorithm, and the starting and ending region of bike journeys are given. We modeled the macro travel patterns of sharing bikes in a spatial-temporal manner and used the non-negative matrix factorization (NFM) method to obtain the basis collective patterns. The patterns show that, different from other modes of shared mobility like ride-sharing, the trip characteristics of bike-sharing could be approximately described by a linear combination of three basis patterns, and bike-sharing mainly serves for the first/last mile short-distance travel around transport hubs, i.e., subway stations. Our findings are helpful for policy makers to better understand the dynamics patterns and influence of sharing-bikes, and to make better arrangements towards facility building and other bike policies.

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In many cities, traffic video surveillance systems have been installed at major intersections. These cameras can capture not only the traffic flow or violations but also the time, location, driving direction, color, and license plate of vehicles. This paper proposes an approach to build trips based on video surveillance data, combine the trips to form daily travel chains, and efficiently classify all travel chains into different modes. A K-means method finds clusters of different types of vehicles, and exclude profitable vehicles, which are always on the road. Four trip chaining patterns are derived from the data and used as the training set. A support vector machine (SVM) method classifies the daily trip chaining patterns. The results show that video surveillance data contains rich information on the traffic patterns and can be used in building the trips. The SVM method can classify trip chaining patterns efficiently with excellent results when processing a large amount of data.@en