Discovery of topological constraints on spatial object classes using a refined topological model

In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in topological relations between objects of a given class and other spatial objects. In order to capture topological relations between more than two (linear) objects, this paper further proposes a refinement of the 9-intersection model for topological relations of line geometries. The discovered topological relations form topological constraints of an object class that can be used for spatial object classification. A case study has been carried out on bridges in the OpenStreetMap dataset for the state of Victoria, Australia. The results show that the proposed approach can successfully learn topological constraints for the class bridge, and that the proposed refined topological model for line geometries outperforms the 9-intersection model in this task

Service quality monitoring in confined spaces through mining Twitter data

Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches

SentiHawkes: a sentiment-aware Hawkes point process to model service quality of public transport using Twitter data

Responsive management of public transport nodes relies on constant monitoring of service quality. Social media content provides a unique opportunity to detect and monitor events impacting service quality in these nodes, as well as predicting future occurrences of such events. However, the confined geographic area of transport nodes exacerbates the sparsity of available feeds, raising two major challenges: limited observations—leading to biased models—and the asynchronous nature of observations—impeding the detection of causal patterns. Thus, this paper proposes a framework based on a multivariate Hawkes point process and sentiment analysis. The multivariate Hawkes point process allows effective modelling of events without making them discrete, hence it is less affected by data sparsity compared to time series models while enabling the prediction of how certain events can trigger future events. Besides, the extracted sentiments from social media feeds provide additional knowledge about passengers’ perception and thus, are used in our approach to strengthening the model. Experiments on a real-world dataset demonstrate the effectiveness of the model in identifying causal relations over the public transport nodes. They also show the efficacy of the proposed solution in predicting events over the limited context compared to state-of-the-art approaches

Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage

Real-time traffic monitoring is essential in many novel applications, from traffic management to smart navigation systems. The large number of traffic cameras being integrated into urban infrastructures has enabled efficient traffic monitoring as an intervention in reducing traffic accidents and related casualties. In this paper, we focus on the problem of the automatic detection of anomalous driving behaviors, e.g., speeding or stopping on a bike lane, by using the traffic-camera feed that is available online. This can play an important role in personalized route-planning applications where, for instance, a user wants find the safest paths to get to a destination. We present an integrated system that accurately detects, tracks, and classifies vehicles using online traffic-camera feed