Abstract
Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activities and trip purposes, and lead to a deeper understanding of human travel patterns. Most existing literature analyses of activity semantics mainly focus on the characteristics of the destination. However, the movement from the origin to the destination can be represented as the flow. The flow can completely represent the activity semantic and describe the spatial interaction between the origin and the destination. Therefore, in this paper, we proposed a two-layer framework to infer the activity semantics of each taxi trip and generalized the similar activity semantic flow to reveal human travel patterns. We introduced the activity inference in the first layer by a combination of the improved Word2vec model and Bayesian rules-based visiting probability ranking. Then, a flow clustering method is used to uncover human travel behaviors based on the similarity of activity semantics and spatial distribution. A case study within the Fifth Ring Road in Beijing is adopted and the results show that our method is effective for taxi trip activity inference. Six activity semantics and four activity semantics are identified in origins and destinations, respectively. We also found that differences exist in the activity transitions from origins to destinations at distinct periods. The research results can inform the taxi travel demand and provide a scientific decision-making basis for taxi operation and transportation management.
Highlights
With the rapid development of information and communication technologies (ICTs) and the widespread use of location-aware devices, there is an increasing availability of mobility data, such as vehicle GPS trajectory data, mobile phone records data and social media check-in data, which can offer high spatiotemporal resolution to observe human travel patterns at the individual level [1]
Inferring travel activity semantics and clustering flow patterns may contribute to a deeper understanding of human travel behavior and mobility, which can assist with transportation planning and management
We proposed a two-layer framework to investigate human travel patterns from an activity semantic flow perspective
Summary
With the rapid development of information and communication technologies (ICTs) and the widespread use of location-aware devices, there is an increasing availability of mobility data, such as vehicle GPS trajectory data, mobile phone records data and social media check-in data, which can offer high spatiotemporal resolution to observe human travel patterns at the individual level [1]. Such fine-grained human mobility data include accurate location and temporal information, the semantic information relating to travel patterns and activity types is usually lacking [2,3,4,5]. Identifying activity semantics and inferring trip purposes from taxi trajectory data is an essential research topic, which can lead to a deeper understanding of human travel patterns
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