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How you travel, where, when, and what you do could indicate who you are. This paper discovers a possible social network between public transit passengers and develops a location–time–activity-based friend recommendation (LTAFR) model based on trips and activities of the passengers. First, trips and activities of passengers are reconstructed from the smart card data. Second, the similarity between passengers is measured in two steps for the activity similarity and trip similarity. The activity similarity is measured considering three dimensions of activity (location, time, and type). The trip similarity is measured considering both spatial and temporal dimensions. Third, a similarity score is defined as the multiplication of the activity and trip similarity values. To discover mutual relations between the passengers, the cosine similarity index is used. Finally, connected Top-k passengers are recommended as potential friends based on the highest cosine similarity values. The proposed model is implemented on a one-day smart card dataset from Brisbane, Australia. Also, the model is implemented on a household travel survey (HTS) dataset for comparing sociodemographic attributes of the recommended passengers. In the end, further investigations show that recommended potential friends have close sociodemographic attributes. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]

Public transit networks play a significant role for urban advertisers because a considerable number of residents in urban areas use public transport for their transportation needs. Automated Fare Collection (AFC) systems provide advertisers with valuable records (smart card data) of the boarding and alighting transactions of passengers in the public transit network. While the demographic attributes of passengers are missed by most AFC systems around the world, the systems can still help to reconstruct the activities and trips of passengers. The availability of smart card data has provided a unique opportunity to create detailed models of passengers' travel behaviour. Hence, it is now possible to develop behavioural advertising techniques in the public transit network based on ongoing activities of passengers. Behavioural advertising in the public transit network considers not only location and time of trips, but also duration and type of passengers' activities. This paper proposes and compares two behavioural advertising models based on the smart card data attributes. The first model, a trip-based one, targets trips of passengers, which means it indicates the maximum number of trips on which an advertisement should be viewed, according to the purpose of each trip. The second model, a passenger-based one, targets passengers by maximizing the number of passengers who will view an advertisement relevant to their trip. Both models are formulated as linear programming models. Both models are run on a case study basis to explicitly present the outcomes of the models and the differences between them. Outcomes of each model determine a set of prime time and locations for advertisements in the public transit network. While the trip-based model targets more relevant trips with simpler computations, the passenger-based model displays advertisements to a greater number of passengers, with more complex computations. [ABSTRACT FROM AUTHOR]