A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The proposed model outperforms other baseline methods including multi-layer LSTM and Lasso with an average testing MAPE of 10.6% when predicting block-level parking occupancies 30 minutes in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.

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The COVID-19 outbreak has necessitated a critical review of urban transportation and its role in society against the backdrop of an exogenous shock. This article extends the transportation literature regarding community responses to the COVID-19 pandemic and what lessons can be obtained from the case of Hong Kong in 2020. Individual behavior and collective responsibility are considered crucial to ensure both personal and community wellbeing in a pandemic context. Trends in government policies, the number of infectious cases, and community mobility are examined using multiple data sources. The mobility changes that occurred during the state of emergency are revealed by a time-series analysis of variables that measure both the epidemiological severity level and government stringency. The results demonstrate a high response capability of the local government, inhabitants, and communities. Communities in Hong Kong are found to have reacted faster than the implementation of health interventions, whereas the government policies effectively reduced the number of infection cases. The ways in which community action are vital to empower flexible and adaptive community responses are also explored. The results indicate that voluntary community involvement constitutes a necessary condition to help inform and reshape future transport policy and response strategies to mitigate the pandemic.

Testing of deep learning models is challenging due to the excessive number and complexity of the computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of how we can automatically select candidate data to test deep learning models. Recent research has focused on defining metrics to measure the thoroughness of a test suite and to rely on such metrics to guide the generation of new tests. However, the problem of selecting/prioritising test inputs (e.g., to be labelled manually by humans) remains open. In this article, we perform an in-depth empirical comparison of a set of test selection metrics based on the notion of model uncertainty (model confidence on specific inputs). Intuitively, the more uncertain we are about a candidate sample, the more likely it is that this sample triggers a misclassification. Similarly, we hypothesise that the samples for which we are the most uncertain are the most informative and should be used in priority to improve the model by retraining. We evaluate these metrics on five models and three widely used image classification problems involving real and artificial (adversarial) data produced by five generation algorithms. We show that uncertainty-based metrics have a strong ability to identify misclassified inputs, being three times stronger than surprise adequacy and outperforming coverage-related metrics. We also show that these metrics lead to faster improvement in classification accuracy during retraining: up to two times faster than random selection and other state-of-the-art metrics on all models we considered.

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