Online business intelligence systems often collect the texts from different sources, such as social media and news websites that can be heterogeneous in practice. These collections bring the difficulties of managing and organizing the comprehensive information hidden in different texts of the system. To more effectively organize the multisourced texts and help online users acquire wider knowledge, we propose a business intelligence system which integrates the multisourced texts from multisources. Regarding in many occasions, multisourced texts share some common contents with respect to the same topics. For example, a tweet and a news report may talk about the same event. Therefore, our goal is to correlate such texts of different sources with respect to the similar topics and get integrated more comprehensive information to facilitate other data mining tasks as well as online applications. To handle the problem, we propose a heterogeneous information network-based text aligning (HINTA) framework in this paper. HINTA applies meta-paths to calculate the text similarities, and constructs correlated pairs between the two types of texts. Next, HINTA first applies anchored pairs as bridges to combine the different types of texts. Finally, three different inference methods are employed to align the multisourced texts. Experimental results on real-world dataset show the effectiveness and efficiency of the framework in addressing the texts alignment problem.

Social networks contain a large amount of information on transportation, e.g., traffic accidents, congestions, and vehicles. Such information is the original ideas of people with respect to real-world transportation issues, and detecting communities on the topic of transportation from the information will benefit many ITS applications. However, realworld social network nodes often contain multiple attributes, and the network can be very large. The two properties can lead to confusion and unscalability problem to clustering methods. In this paper, we propose a semisupervised method, namely, Transportation Community Detection (TRACED), to address this problem. TRACED allows multiple individuals to select their familiar nodes as the participants of a certain community, and thus, the confusion of multiple attributes can be largely reduced. Moreover, the proposed method can be expanded to large networks since it is able to conduct an effective clustering with low time complexity. With the help of TRACED, we can detect densely connected communities on the topic of transportation for further studies.

Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.

Detecting communities (or modular structures) and structural hole spanners, the nodes bridging different communities in a network, are two essential tasks in the realm of network analytics. Due to the topological nature of communities and structural hole spanners, these two tasks are naturally tangled with each other, while there has been little synergy between them. In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously. Specifically, we apply a harmonic function to measure the smoothness of community structure and to obtain the community indicator. We then investigate the sparsity level of the interactions between communities, with particular emphasis on the nodes connecting to multiple communities, to discriminate the indicator of SH spanners and assist the community guidance. Extensive experiments on real-world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the community detection task and also in the SH spanner identification task (even the methods that require the supervised community information). Furthermore, by removing the SH spanners spotted by our method, we show that the quality of other community detection methods can be further improved.

With the booming of social media, sentiment analysis has developed rapidly in recent years. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose the traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new prospective for modern ITSs. Methods and models in TSA are proposed in this paper, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection. Two cases were studied to demonstrate the efficiency of our method: the “yellow light rule” and “fuel price” in China. Our work will help the development of TSA and its applications.

Social activity-travel has gained more and more attention as it is a growing percentage of the whole travel. To study its generation mechanism and behavioral characteristics, social network data are usually essential. However, due to individual privacy, it is rather difficult for traditional methods such as questionnaires to collect abundant reliable data. Therefore, we propose a novel method to grow realistic social networks based on artificial transportation systems (ATS). By incorporating the activity-travel simulation provided by ATS and a new agent-based model for social interaction, the method takes into account human mobility to generate spatially embedded social networks. Human mobility shapes and impacts social networks dynamically but is usually ignored by related studies. A case study based on computational experiments is carried out to verify the method. The results indicate that the method can generate social networks with similar topological and spatial characteristics to real social networks.