Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones.

Metro bears a large number of passenger flows in urban transportation systems. Short-term metro origin-destination (OD) passenger flow prediction is an essential component of intelligent transportation systems (ITS), which allows operators to better monitor the metro system and improve the level of service for passengers. In this paper, we exploit a novel data structure, hypergraph, to represent the complex correlation between OD pairs, and propose an elaborately designed Spatio-Temporal Dynamic Attentive Multi-HyperGraph Network (ST-DAMHGN) to tackle the short-term OD passenger flow prediction problem. In the proposed framework, we construct multiple hypergraphs to model the relationship between OD pairs and adopt the perceptual field to realize efficient and effective vertex feature extraction. Then, we utilize the attention mechanism to adaptively and dynamically synthesize information from multiple hypergraphs and make a trade-off between exploration and exploitation. A case study is conducted on the metro system of Hangzhou, China. The results of extensive experiments show that ST-DAMHGN outperforms baseline models. The efficiency is validated for the multi-hypergraph model, perceptual field, spatial feature extraction, and attention mechanism. The hypergraph structure used in our model is verified suitable for modeling traffic data without physical road networks to map directly, e.g., OD passenger flow. ST-DAMHGN can be widely implemented by defining proper correlations for model relationships.

Recent research has shown that some network traffic flow patterns are similar across multiple traffic regions. Identifying and transferring these domain-invariant features can significantly boost model accuracy and robustness, providing new insights into dealing with modeling issues like traffic data insufficiency and dataset shift. However, how to acquire transferable network traffic flow patterns from multiple traffic regions and adapt such knowledge to downstream prediction tasks of target regions remains challenging. To realize domain-invariant traffic flow pattern transfer and provide more robust prediction under insufficient data conditions, we propose a macroscopic fundamental diagram (MFD) guided transfer learning method, namely physics-guided multi-source domain adversarial network (PG-MDAN). First, an MFD similarity measure is proposed to determine what traffic flow patterns are transferable and to what extent they can be transferred. PG-MDAN embeds this physics-informed transferability measure in domain adversarial pre-training for better adaptation ability. Numerical experiments based on two real-world urban network traffic datasets show that PG-MDAN can successfully transfer recurrent and non-recurrent network traffic flow patterns from multiple regions to provide more robust and responsive prediction performance. Finally, extensive sensitivity analysis is conducted, and the results validate that applying such physical regularization can effectively avoid negative transfer and provide a flexible tool to initiate traffic flow pattern transfer in practice.

The mobile internet and social media have changed people’s travel behavior. During a journey, tourists generate a large amount of ‘digital footprint’ that can be used for studying tourist behavior. Exploring tourist spatial behavior based on social media big data is a new research field. Using the example of Xi’an, this study attempts to reveal the characteristics of tourists’ spatial network of tourist flow behavior rules and the function of travel areas and nodes by analyzing the spatial network of tourist flow using social network analysis and data visualization based on microblog-geotagged big data generated by tourists. Geotagged messages are obtained from the Open API of the Sina Microblog. This study found that tourism development shows characteristic of spatial aggregation. The Bell Tower, Qujiang, and Lintong areas are the most crucial tourism development areas in Xi’an. Furthermore, there are obvious hierarchical characteristics in a tourism spatial network. Travel nodes can be divided into three levels, namely pivotal, important and ordinary nodes. Famous scenic spots and traffic hubs are the most important sites that tourists visit. Moreover, the travel route can be divided into four types: synthetic routes, urban-center routes, the Qujiang travel route, and the Lintong travel route. The findings can provide a foundation for tourism cooperation, tourism planning, accurate marketing, travel route design, and other works.