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Transportation accounts for more than a quarter of the greenhouse gas emissions that are causing climate change. Carpooling is a subset of the sharing economy, in which individuals share their vehicle with commuters to save travel expenses. In recent decades, carpooling has been promoted as a feasible alternative to car ownership with the potential to alleviate traffic congestion, parking demand, and environmental problems. Unstable economic conditions, cultural norms, and lack of infrastructure make cultural exchange activities and mobility habits different in developing nations to those in developed countries. The rapid evolution of sharing mobility has reshaped travelers’ behavior and created a dire need to determine the travel patterns of commuters living in megacities in developing countries. To obtain data, a web-based stated choice (SC) experiment was used in this study. It used mode-related variables, socioeconomic demographic variables, and a coronavirus disease 2019 (COVID-19) precautionary measure variable. Logit models, namely the mixed logit regression model (ML) and the multinomial logit regression model (MNL), were applied to analyze the available data. According to modeling and survey data, economic variables associated with modes of transport, such as trip time and trip cost, were determined to be significant. Additionally, the results revealed that commuters were more conscious of COVID-19 preventive measures, which was determined to be highly significant. The findings showed that the majority of residents in the COVID-19 pandemic continue to rely on automobiles and motorcycles. It is noteworthy that individuals with more than two members in their family and a travel distance of less than seven miles were more likely to prefer a carpooling service. This study’s findings will provide a basis for researchers to aid existing operators in the field of transportation, as well as offer guidelines for governments in developing countries to enhance the utility of transportation networks.

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This article mainly discusses how to extract the interested information from massive amounts of micro-blogs and recommend right information to user, which is a hot research area in recommendation systems and social networks, too. To solve this problem, a model called Multi-tags Latent Dirichlet Allocation is proposed. Using this model, topics paid attention by users can be mined effectively and the defect of low degree of differentiation for the short blog content is settled. Experiments showed that the tags of user's micro-blog can be figured out with this model which makes users manage their resources at their convenience and others find their needed resources through tags. The results, experimented on real micro-blog data set, indicate that this model works better than traditional model on extracting tags. Standard measuring index Perplexity is applied to this model to estimate the likelihood of new text. If the number of topics is selected appropriately, the accuracy will be raised to almost 10%.