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• Developed an approach to approximate the demand of a carsharing station. • Improved the estimation accuracy of the model by including social media data and spatio-temporal correlated data. • Applied a spatio-temporal autocorrelation model. • Developed a correlation analysis to account for the interactions among variables. As an emerging urban mobility service, carsharing has become increasingly popular worldwide. To understand customers' needs and optimize the design of service networks, the usage of carsharing vehicles whose trips are recorded by the operators has been applied in research estimating carsharing demand. However, as a form of spatio-temporal correlated data, the underlying spatio-temporal information included in such carsharing records has not been investigated in existing models of carsharing demand. Meanwhile, due to the supply limitation of carsharing stations, some demand cannot be fulfilled and thus remains unrecorded in the operational data. Unrealized demand may lead to underestimation of carsharing demand and therefore an incorrect vehicle deployment strategy by the service providers. In view of these issues, this paper develops an innovative approach to estimating the actual demand at a carsharing station with operational data from GoGet , the largest carsharing company in Australia. The accuracy of the estimation is improved by adding spatio-temporal correlated variables as well as variables from emerging data sources such as social media. To explore the latent space-and-time correlated information, spatio-temporal autoregressive and moving-average models have been applied. Based on the results of the analysis, the paper also provides recommendations related to the operation policies of the service providers. [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]